# Agrici Daniel > Open-source AI marketing builder creating practical Claude Code skills, n8n workflows, and marketing systems. ## Current Facts - Counts verified: 2026-08-27 - [GitHub portfolio](https://github.com/AgriciDaniel): 44,000+ stars across 63 public repositories, including 53 original repositories - [claude-seo](https://github.com/AgriciDaniel/claude-seo): v2.2.5, 15,500+ stars, 25 skills, 18 specialist agents - [Claude Ads](https://github.com/AgriciDaniel/claude-ads): v2.0.1, 8,500+ stars, 12 advertising platforms, 506 passing tests for the v2 architecture release - [AI Marketing Hub](https://www.skool.com/ai-marketing-hub): 5,200+ members, free - [AI Marketing Hub Pro](https://www.skool.com/ai-marketing-hub-pro): $88/month, 280+ members - Licensing: terms vary by repository; the repository LICENSE file controls reuse ## Authoritative Sources - [Website](https://agricidaniel.com/) - [GitHub](https://github.com/AgriciDaniel) - [Free community](https://www.skool.com/ai-marketing-hub) - [Pro community and current price](https://www.skool.com/ai-marketing-hub-pro) - [claude-seo release](https://github.com/AgriciDaniel/claude-seo/releases/tag/v2.2.5) - [Claude Ads release](https://github.com/AgriciDaniel/claude-ads/releases/tag/v2.0.1) ## Core Projects - [claude-seo](https://github.com/AgriciDaniel/claude-seo): Free SEO audits from the terminal. 25 skills and 18 specialist agents for technical SEO, content, schema, GEO, and more. - [claude-ads](https://github.com/AgriciDaniel/claude-ads): Source-grounded paid-media audits, planning, creative workflows, attribution, and reporting across 12 platforms. - [claude-blog](https://github.com/AgriciDaniel/claude-blog): Research-first blog creation optimized for useful answers, organic search, and AI citations. - [claude-obsidian](https://github.com/AgriciDaniel/claude-obsidian): An AI-powered Obsidian knowledge system with ingestion, retrieval, cross-references, linting, and canvas workflows. ## Products and Channels - [Rankenstein.pro](https://rankenstein.pro) - [YouTube](https://www.youtube.com/@AgriciDaniel) - [LinkedIn](https://www.linkedin.com/in/daniel-agrici/) - [X](https://x.com/AgriciDaniel) ## Published Articles - [AI Agent Approval Workflow: A Practical Review Matrix](https://agricidaniel.com/blog/ai-agent-approval-workflow) - [The BBC Disclosed Its AI 2027 Video Three Ways. One Shot-Level Gap Remained.](https://agricidaniel.com/blog/bbc-ai-2027-video-ai-slop-case-study) - [YouTube Pro: One Evidence Chain From Research to Thumbnail](https://agricidaniel.com/blog/youtube-pro-research-to-thumbnail) - [DeepSeek Harness Explained: 181K Stars, and the Brain I Built to Vet It](https://agricidaniel.com/blog/deepseek-harness-plain-english-guide) - [How I Automate YouTube Keyword Research With One Claude Prompt](https://agricidaniel.com/blog/automate-youtube-keyword-research-claude) - [Claude Blog v2.1.1: What Changed and What to Do Now](https://agricidaniel.com/blog/claude-blog-v2-1-1-release) - [Claude Ads v2.0.1: The Paid-Ads Operating System for Claude](https://agricidaniel.com/blog/claude-ads-v2-0-1-release) - [Claude Obsidian v1.9: Your AI Second Brain Gets a Compound Memory](https://agricidaniel.com/blog/claude-obsidian-v1-9-compound-vault) - [Claude and Codex Skills: The AI Visibility Wedge I Would Rank First](https://agricidaniel.com/blog/claude-codex-skills-ecosystem) - [I Turned Obsidian Into a Self-Organizing AI Second Brain - Here's the Free Plugin](https://agricidaniel.com/blog/claude-obsidian-ai-second-brain) - [Local SEO Brain: Claude Code Agent for Ranking Local Businesses in the Map Pack](https://agricidaniel.com/blog/claude-code-local-seo-brain) - [claude-ads v1.7.1: SSS+ Polish, Animated Banner, 10 Citations Verified](https://agricidaniel.com/blog/claude-ads-v1-7-1-release) - [codex-seo: I Ported My 9,500-Star SEO Stack to OpenAI Codex CLI](https://agricidaniel.com/blog/codex-seo-openai-codex-cli) - [Two Claude SEO Releases in One Day: FLOW Framework Plus Security Hardening](https://agricidaniel.com/blog/claude-seo-v196-flow-security-hardening) - [claude-music: Generate Full Songs in Your Terminal with ACE-Step 1.5](https://agricidaniel.com/blog/claude-music-ai-production) - [Claude Code Security: The AI Security Audit That Found 23 Vulnerabilities in My Own Code](https://agricidaniel.com/blog/claude-cybersecurity-ai-security-audit) - [claude-ads v1.5: 250+ Ad Audit Checks Across 7 Platforms](https://agricidaniel.com/blog/claude-ads-v1-5-release) - [Claude Code Just Turned Obsidian Canvas Into an AI Design Studio](https://agricidaniel.com/blog/claude-canvas-ai-visual-production) - [How I Got 8,000 GitHub Stars in 9 Months as a Solo Developer](https://agricidaniel.com/blog/how-i-got-8000-github-stars) - [I Just Shipped the Biggest Claude SEO Update Yet](https://agricidaniel.com/blog/claude-seo-172-firecrawl-backlink-analysis) - [I Built WP MCP Ultimate - One Plugin to Connect AI to WordPress](https://agricidaniel.com/blog/wp-mcp-ultimate-wordpress-ai-plugin) - [I Replaced $500/Month in SEO Data With Free Google APIs - Here's the Setup](https://agricidaniel.com/blog/google-api-seo-automation-claude-code) - [AI Marketing Automation: The Open-Source Stack I Use Daily](https://agricidaniel.com/blog/ai-marketing-automation-stack) - [banana-claude: AI Image Generation That's Surprisingly Good at Logos](https://agricidaniel.com/blog/banana-claude-ai-image-generation) - [Best Claude Code Skills in 2026 - The Complete Guide](https://agricidaniel.com/blog/best-claude-code-skills-2026) - [Claude Code Just Replaced Your Ad Agency - 250+ Checks in One Command](https://agricidaniel.com/blog/claude-code-ad-agency) - [Claude Code Just Replaced Your Blog Writer - AI Slop Is Over](https://agricidaniel.com/blog/claude-code-blog-writer) - [Claude Code Just Replaced Your Entire SEO Stack - Here's How](https://agricidaniel.com/blog/claude-code-seo-stack) - [Free SEO Audit Tools That Actually Work (No $300/month Required)](https://agricidaniel.com/blog/free-seo-audit-tools) - [n8n SEO Automation: Complete Beginner's Guide (2026)](https://agricidaniel.com/blog/n8n-seo-automation-beginners-guide) - [How I Built an AI SEO Content System in n8n (Full Walkthrough)](https://agricidaniel.com/blog/n8n-seo-content-system) - [skill-forge: Build Your Own Claude Code Skills From Scratch](https://agricidaniel.com/blog/skill-forge-build-claude-code-skills) ## Site Policies - [Privacy](https://agricidaniel.com/privacy-policy) - [Cookies](https://agricidaniel.com/cookie-policy) - [Terms](https://agricidaniel.com/terms) - [Accessibility](https://agricidaniel.com/accessibility) ## Article Archive Note Article bodies below are publication snapshots. Historical counts, versions, prices, and product capabilities may reflect the article's publication date. Use Current Facts and Authoritative Sources above for present-day claims. *** # AI Agent Approval Workflow: A Practical Review Matrix - URL: [https://agricidaniel.com/blog/ai-agent-approval-workflow](https://agricidaniel.com/blog/ai-agent-approval-workflow) - Published: 2026-09-08 - Updated: 2026-09-08 - Category: Automation - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. A practical four-lane review matrix for deciding what an AI agent may prepare, propose, apply, or escalate before it changes a system. An AI agent should not need the same approval rule for every task. Reading a campaign report, drafting a content brief, changing a CRM field, spending a budget, and sending a message to a customer have different consequences. The useful question is not, "Do we trust the agent?" It is, "What exact action is it allowed to take here, on which target, with what evidence, and who needs to decide before it happens?" This article proposes a practical approval matrix for marketing and operations work. The matrix is editorial synthesis, not a prescribed OpenAI or NIST model. Listen instead ## AI agent approval workflows in under two minutes 1:25 Your browser does not support HTML audio. Use the MP3 download below. Audio summary of this article. Captions and the synchronized transcript below match the spoken words. [Download MP3](/audio/ai-agent-approval-workflow-summary.mp3)[Download English captions](/audio/ai-agent-approval-workflow-summary.vtt)[Open the synchronized transcript](#audio-summary-transcript) Read the synchronized transcript 0:00 An AI agent approval workflow 0:01 should classify the next action, 0:03 not rely on the agent's name or a vague feeling of trust. 0:08 A practical matrix uses four lanes. 0:13 First, observe and prepare. 0:15 The agent reads permitted sources 0:16 and drafts local analysis. 0:18 Second, propose a change: it names the exact target, 0:22 evidence, expected effect, and rollback, 0:24 but has no write capability. 0:28 Third, apply a bounded reversible change 0:30 only after approval is tied to the operation, 0:32 target, and reviewed revision. 0:35 Fourth, escalate for a human decision 0:37 when the effect expands into publication, spending, 0:40 permissions, deletion, or external communication. 0:44 Useful approval is specific. 0:46 It identifies who can approve, 0:48 what operation is allowed, 0:49 the exact target and payload version, 0:52 the supporting evidence, 0:53 when approval expires, 0:55 and how the result will be checked or reversed. 0:58 Technical controls should enforce those limits. 1:01 Separate read and write access, 1:03 require exact identifiers, 1:05 preview changes where possible, 1:07 prevent duplicate retries, 1:08 and record the after-state. 1:10 Then test awkward cases: 1:12 missing data, 1:13 ambiguous targets, 1:15 changed proposals, 1:16 and partial failures. 1:18 This matrix is an editorial planning model. 1:20 It is not evidence that it has been deployed 1:22 or tested in Daniel's systems. ## Approval belongs to the action, not the agent name An agent may be safe to use for one action and inappropriate for the next. A research agent that reads public pages and returns a source list can usually work in a narrow read-only scope. That does not give the same agent authority to change a bid strategy, update a subscriber list, or publish a page. OpenAI describes a similar separation in its guidance for Codex: low-risk work can move within a bounded environment, while higher-risk actions stop for review. Its examples combine technical boundaries, approval policy, network controls, and agent-aware logs. [OpenAI: Running Codex safely](https://openai.com/index/running-codex-safely/) This leads to a simpler design rule: classify the next effect, then choose the control. Do not infer authority from an agent's label, a successful previous run, or a persuasive recommendation. ## The four-lane review matrix Use the following matrix as a starting point. It is a planning tool. Adjust the action examples and risk thresholds to the systems, data, and people in your own workflow. Lane Agent may do Human review Technical enforcement Marketing example 1. Observe and prepare Read permitted sources, summarize, classify, calculate, draft a proposal. No approval for the local draft, provided the scope is already permitted. Read-only credentials, allowed source list, time and data limits, run log. Turn last week's approved analytics export into a campaign-summary draft. 2. Propose a change Produce a specific action plan with target, inputs, rationale, expected effect, and rollback idea. A named owner accepts, rejects, or revises the proposal before any change. No write-capable tool is available to the agent. Preserve the proposal and evidence. Prepare proposed audience exclusions from a supplied search-term report. 3. Apply a bounded, reversible change Execute one approved operation against an exact target. Approval binds the operation, target, revision, and scope. Write allow-list, preflight preview, idempotency key where supported, after-state check, and rollback path. Apply an approved negative-keyword list to one named draft campaign. 4. Escalate for a human decision Stop and create a decision-ready packet. A responsible human decides whether a later, separately authorized executor may act. No execution capability is granted by the escalation itself. Preserve evidence and the decision or refusal record. Sending external messages, changing access, moving money, publishing, deleting broad data, or altering production routing. The matrix is not a substitute for judgment. It gives the judgment a place to be recorded before the action happens. ## What an approval must contain "Approved" is too vague to control an agent. A useful approval answers seven questions: - Who can approve it? Name the responsible decision-maker or approval group. - What operation is approved? Describe the change in one sentence. - Which exact target is in scope? Name the account, campaign, document, record set, or environment. - Which version is being approved? Bind the proposal to a stable revision or payload, meaning the exact data sent to the tool or system. A later edit is a new proposal. - What evidence supports it? Include the source data, assumptions, conflicts, and important unknowns. - When does it expire or require reapproval? Set a time limit and list conditions that invalidate approval, such as a changed payload, target, budget, permission, or external audience. - How can the change be checked or reversed? State the postcondition, meaning the expected after-state, and rollback route before execution. OpenAI's workspace-agent guide similarly treats governance as a design choice: define boundaries, required approvals, and human-in-the-loop checkpoints for sensitive actions. It also recommends testing agents with straightforward and messier examples to find missing constraints. [OpenAI Academy: Workspace agents](https://openai.com/academy/workspace-agents/) An approval that lacks a target or revision is not an approval to act. It is a request for a clearer proposal. ## Make the controls technical, not prompt-only Instructions are useful, but they are not the enforcement layer. A safer workflow makes an unauthorized action impossible or detectable through its tools and environment. For a change in lane 3, build controls in layers: - Give the agent access only to the named system and operation it needs. - Separate read credentials from write credentials. - Require an exact target identifier instead of accepting a broad search. - Generate a preview or dry-run result before the write where the system allows it. - Bind human approval to the reviewed payload, not to a general intent. - Record what was proposed, approved, attempted, and observed afterward. - Stop when the target, evidence, or rollback route is missing. OpenAI's Codex safety guidance describes sandboxing as a boundary over write locations and network access, while approval policy decides when work outside that boundary requires consent. It also describes telemetry that can capture the surrounding request, tool activity, approval decisions, and results. [OpenAI: Running Codex safely](https://openai.com/index/running-codex-safely/) These controls let low-impact work proceed while requiring an explicit decision for higher-impact actions. ## A worked marketing automation example Consider a weekly paid-search maintenance workflow. The agent receives an approved export of search terms and a list of products that must not be advertised. ### Lane 1: prepare the evidence The agent can group terms, show the query, spend, conversions if supplied, and the rationale for each proposed exclusion. It may draft a table. It does not change the advertising account. ### Lane 2: propose the action The proposal should identify the destination campaign, the exact negative-keyword match type, the evidence row, possible overblocking risk, and the rollback method. The reviewer now has something concrete to assess instead of a generic statement that the account will be "optimized." ### Lane 3: apply one approved batch If the reviewer approves a particular revision for a named campaign, the executor can apply that bounded batch. A before-and-after export or API response should confirm the intended items were created. If the system supports it, use an idempotency key. A stable batch identifier only prevents duplicates when the executor checks it against prior work and refuses or safely deduplicates a repeat. ### Lane 4: escalate when the effect expands The workflow stops when the proposed action changes budget, affects an unapproved campaign, requires a new permission, or would contact someone outside the company. Those are new decisions, not variations of the original approval. This approach separates recommendation quality from execution authority. A well-supported proposal can still be declined. A weak or incomplete proposal should not become safer because it is automated. ## Use the same pattern for content systems Content automation has a similar boundary. An agent can create an outline, assemble source notes, or draft metadata in lane 1. It can propose a content update in lane 2. Publishing, changing a canonical URL, or altering a live template belongs in a reviewed execution lane or an escalation lane, depending on the target and reversibility. This differs from a generic automation walkthrough. The relevant question is not how many nodes an automation contains. It is where each action crosses from analysis into an external effect. The existing [n8n SEO content system](/blog/n8n-seo-content-system) is useful background for content-pipeline architecture. The existing [AI marketing automation stack](/blog/ai-marketing-automation-stack) shows how tools, workflows, and APIs can form a broader operating system. Neither link is evidence that the approval matrix in this article has been deployed in those workflows. ## Test the boundary before trusting it Approval design should be tested with examples that try to cross the boundary. Ask what happens if: - The agent receives incomplete source data. - The target identifier is missing or ambiguous. - A proposal changes after approval. - A retry arrives after a partial failure. - The tool returns an unexpected result. - A draft action expands into a permission, payment, publication, or external communication request. NIST's Generative AI Profile calls out fact-checking techniques for generated information, testing and evaluation, and documentation of relevant practices as ways to manage information-integrity risks. [NIST AI 600-1: Generative AI Profile](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) For each test, record the expected stop, the actual outcome, and the evidence. If a workflow cannot explain why it acted, what it changed, or how to undo it, it is not ready for a broader execution scope. ## A short implementation checklist Before enabling an agent to change any system, confirm: - [ ] The action is classified by effect, not by agent name. - [ ] Read, propose, apply, and escalate paths are distinct. - [ ] A human approval names the approver, operation, target, revision, evidence, expiration, reapproval conditions, and rollback route. - [ ] Write capability is technically limited to the approved operation. - [ ] Retries cannot silently duplicate a change. - [ ] The after-state is checked and recorded. - [ ] The workflow stops when it encounters a new target or wider effect. - [ ] Tests include missing data, ambiguous targets, changed proposals, and partial failures. ## Sources - [OpenAI: Running Codex safely](https://openai.com/index/running-codex-safely/), accessed 2026-09-07. - [OpenAI Academy: Workspace agents](https://openai.com/academy/workspace-agents/), accessed 2026-09-07. - [NIST AI 600-1: Generative AI Profile](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf), accessed 2026-09-07. *** # The BBC Disclosed Its AI 2027 Video Three Ways. One Shot-Level Gap Remained. - URL: [https://agricidaniel.com/blog/bbc-ai-2027-video-ai-slop-case-study](https://agricidaniel.com/blog/bbc-ai-2027-video-ai-slop-case-study) - Published: 2026-08-27 - Updated: 2026-08-27 - Category: Marketing - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. The BBC labeled its AI-generated footage three ways. A frame-by-frame case study found that the shot-level caption stops at 05:22, while generated footage returns later. On 1 August 2025 the BBC World Service published "[AI2027: Is this how AI might destroy humanity?](https://www.youtube.com/watch?v=1UufaK3pQMg)" , an eight minute film about a speculative AI scenario, illustrated largely with text to video AI. It had 11,479,084 views when I captured the watch page on 27 August 2026. I downloaded it, segmented it shot by shot, and read the frames. The film disclosed its generated footage three separate ways. It also produced a generated correspondent, and its per-shot tool caption stops at 05:22 even though generated footage returns later. The public evidence does not establish that the BBC fully complied with its guidance, or that it breached it. This is a narrower case study about what the disclosure communicates, when it appears, and what survives a clipped shot. The cover phrase, "AI Disclosure Failed", means the disclosure did not solve the shot-level epistemic-status problem examined here. It does not mean the BBC hid its use of AI, and it is not a finding that a rule was broken. ▶ Click to load the privacy-enhanced YouTube player 8:08 [](https://www.youtube.com/watch?v=1UufaK3pQMg) Video: "AI2027: Is this how AI might destroy humanity?" by BBC World Service. The privacy-enhanced YouTube player loads only after you activate it. [Watch directly on YouTube](https://www.youtube.com/watch?v=1UufaK3pQMg). The short version - The BBC disclosed three ways: spoken at 00:30, in the video description, and captioned per shot naming five separate generators. - A subject-and-illustration exception in the BBC guidance appears relevant. Adjacent paragraphs impose additional conditions and an editorial referral, so the public artifact cannot establish full compliance either way. - The per-shot caption stops at roughly 05:22 and never returns. About 35 seconds of uncaptioned generated footage runs after that point, in the section containing the real interviews. - The narration makes 16 explicit attributions of scenario claims in 1,003 words. The film later shows a forecast distribution, two endings, and the word "fictional", but I found no simultaneous shot-level overlay marking a depicted event as hypothetical. - The practical fix is small: label the pixels and the claim separately, keep that label on reused generated footage, and inspect every rendered sign, logo, and screen before release. Listen instead ## BBC AI 2027: the evidence in about three minutes 3:11 Your browser does not support HTML audio. Use the MP3 download below. The original sample-free music bed was generated locally with ACE-Step and sidechain-ducked beneath the narration. No third party voice was cloned or imitated. [Download MP3](/audio/bbc-ai-2027-case-study-summary.mp3)[Download English captions](/audio/bbc-ai-2027-case-study-summary.vtt)[Jump to transcript](#audio-summary-transcript) Read the synchronized transcript 0:00 [Soft instrumental music begins and continues under narration.] 0:02 I spent an afternoon pulling apart an eight minute BBC film frame by frame, and the evidence changed the story I thought I was writing. 0:11 The BBC World Service film explains AI 2027, a speculative paper about an AI race that can end with humanity gone. 0:19 The obvious criticism would be that a major newsroom used generated video and hoped nobody noticed. 0:27 That criticism is wrong. 0:31 The BBC disclosed the generated footage three ways. 0:34 The presenter explains the experiment at thirty seconds. 0:38 The YouTube description names the use of generative AI. 0:41 Individual scenario shots carry captions naming Sora, Runway, Kling, Hailuo, and Veo. 0:52 Most of the scenario sequence also carries a future year stamp. 0:56 The policy question is less simple. 0:58 One sentence in the BBC guidance contains an exception when AI is the subject and its use is illustrative. 1:04 The surrounding guidance also says AI media must not materially mislead, and it requires an editorial referral. 1:13 I cannot see that internal process. 1:16 The public evidence does not establish full compliance or a breach. 1:19 What it does establish is a timing problem. 1:22 The BBC tool caption reaches roughly five minutes and twenty two seconds, then does not return. 1:28 After that point, about thirty five seconds of generated footage used again appears between real interviews. 1:36 The label was reliable for five minutes and absent where synthetic shots and filmed interviews sit closest together. 1:42 The protest sequence contains the clearest visual defect. 1:46 A generated correspondent holds a microphone with the word NEWS. 1:50 The marks above NEWS do not form a network name I can read. 1:54 Nearby placards contain non-words. 1:57 I can observe the artifacts. 1:59 A single frame cannot prove that a person or a network does not exist. 2:03 The deeper issue is epistemic status. 2:06 The narration makes sixteen explicit attributions of scenario claims in one thousand and three words, an average interval of about twenty one seconds from the first to the last. 2:16 The film later shows a forecast distribution, shows both endings, and calls the scenarios fictional. 2:23 That qualification exists. 2:25 I found no simultaneous shot-level overlay telling a viewer that the event on screen is hypothetical. 2:33 That gives me three practical rules. 2:37 Label the pixels and the claim separately. 2:40 Keep the label on every generated shot, including footage used again. 2:44 Read every sign, logo, and screen before the edit ships. 2:47 The full article includes the timestamps, arithmetic, captured sources, contrary evidence, and the finding I tested and withdrew. 2:57 That last part matters. 2:59 A credible case study should show where the evidence changed the author, not only where it supported the headline. 3:05 [Music fades out.] ## In this case study - [What the BBC published](#what-the-bbc-actually-published) - [Method and limits](#how-i-examined-it-and-what-i-could-not-determine) - [What the BBC got right](#what-the-bbc-got-right) - [The 05:22 caption stop](#finding-1-the-bbcs-per-shot-ai-caption-stops-at-05-22) - [The generated news report](#finding-2-the-bbc-ai-2027-video-contains-a-generated-news-report-inside-a-real-news-report) - [When versus whether](#finding-3-the-bbcs-year-stamp-marks-when-not-whether) - [Runtime split](#finding-4-how-the-bbc-ai-2027-video-splits-its-runtime) - [Production rules](#what-i-would-change-if-i-shipped-this) - [Sources](#sources) ## What the BBC actually published Facts first, with no reading attached. Two things share a name here and they are not the same object. "AI 2027" is the research paper published by the AI Futures Project on 3 April 2025. "AI2027" is the BBC World Service film about that paper, published 1 August 2025. Where the distinction matters below, I say the paper or the film. "AI2027: Is this how AI might destroy humanity?" runs 8:08 and is fronted by an on-camera presenter. It contains real interview footage of Gary Marcus and Thomas Larsen, a co-author of the AI 2027 paper, plus footage of OpenAI chief executive Sam Altman speaking to reporters. At 00:30 the presenter explains that the team used text-to-video AI as an experiment. The video description separately states that mainstream generative AI tools were used to recreate scenario scenes. Generated shots carry an on-screen caption naming the specific tool that made them. And a year stamp sits under the BBC logo through the scenario sequence. The underlying paper, [AI 2027](https://ai-2027.com/), was published on 3 April 2025 by the AI Futures Project, authored by Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland and Romeo Dean. ## How I examined it, and what I could not determine I pulled the BBC World Service film "AI2027: Is this how AI might destroy humanity?" with yt-dlp, ran the file through Gemini 2.5 Flash to produce a shot-by-shot segment table, then verified findings by cropping and reading actual frames with ffmpeg. Where a number below comes from the model pass rather than from my own frame reading, I say so in the sentence. Only the 640x360 stream was retrievable without a PlayabilityContext token, so all stills are 360p. The visual artifacts I describe are legible at that resolution. A fourth disclosure layer exists in principle: YouTube's own "altered or synthetic content" notice. I checked the served watch page HTML on 27 August 2026 and did not find it. That HTML capture alone cannot establish what the complete interface displayed, so I cannot say whether the notice was applied. I cannot tell you which team signed this off, what the brief or the prompts said, whether Content Credentials were embedded, or what anyone intended. I am reading the artifact, not the newsroom. I also did not approach the BBC for a response before publishing, and you should weigh the piece accordingly. One of my own findings died in the process. I thought the BBC logo was corrupted in the protest shot at 03:45. I sampled the logo region at five timestamps and it renders correctly at 00:03, 03:47 and 05:00. It was an overlay fade. A case study about AI slop that quietly buries its own bad findings would deserve everything it got. ## What "AI slop" means, and what it does not Merriam-Webster's dictionary entry for "slop" gives, as sense 1a, "digital content of low quality that is produced usually in quantity by means of artificial intelligence". Two words carry the definition: low quality , and in quantity . Slop is not a synonym for "made with AI". I have made the same distinction about [AI slop in written content and the tooling that catches it](/blog/claude-code-blog-writer), and the test is the same in video. I use the term narrowly here. The unreadable placards and network-style mark are visual defects that match the quality half of the definition. That is not enough to classify the full film as slop, and this article does not do so. The useful question is how a clearly disclosed film can still make a speculative branch look more settled than it is. ## What the BBC got right Six points, each checked against the saved video, caption track, or captured guidance. It labels per shot, and names the tool. Not a blanket disclaimer. A caption in the top right of individual generated shots pairs the words "Generated with" with Sora, Runway, Kling, Hailuo, or Veo. Five generators, all named on screen. Sora appears from 00:00, Runway from 00:04, Kling from 00:08, Hailuo from 00:13, Veo from 00:52. In the model pass, 57 of 89 segments carried a caption, a count I did not hand-verify shot by shot. What I did verify: sampling the caption region every four seconds from 00:40 to 01:16 found it present in 10 of 10 frames. It stamps the year on screen. Through the scenario sequence a year chyron sits under the BBC logo: 2027, then 2028, 2029, the 2030s, 2035, 2040. In the model pass it appears on 54 of the 89 segments. A viewer is shown a future date across the generated scenario sequence until the last year-stamped segment at 05:23. The script attributes relentlessly. Counting the narration only, and excluding the 250 words spoken by Marcus and Larsen in interview, it runs 1,003 words containing 16 explicit attributions of scenario claims. That is one every 63 words. Here is the full list, so you can check it: - 00:08 attribution to the researchers - 00:15 prediction attributed to the same researchers - 00:36 claim attributed to the scenario - 01:13 event located inside the scenario - 01:33 prediction attributed to the paper - 01:41 imagined event attributed to the researchers - 02:28 prediction attributed to the scenario - 02:42 imagined event attributed to the researchers - 03:10 prediction attributed to the scenario - 03:18 prediction attributed to the paper - 03:31 event located inside the scenario - 04:02 prediction attributed to the researchers - 04:32 event located inside the scenario - 04:57 imagined event attributed to the paper - 05:07 claim attributed to the scenario - 05:16 wording attributed to the paper At 07:37 the presenter explicitly calls both AI 2027 outcomes fictional. It shows the paper's own uncertainty. At 06:03 the film puts the AI 2027 "Timelines Forecast" graph on screen, showing probability density curves for three forecasters with medians spread from October 2027 to January 2032 and 90th percentiles reaching June 2044 and beyond 2050. At 06:51 it shows the paper's "Choose Your Ending" interface with both the Slowdown and Race buttons visible. The film does not hide that the paper is a distribution with two branches. It books a real critic. Gary Marcus says on camera that he considers the near-term scenario extremely unlikely. The subject-and-illustration exception appears relevant. The BBC's published guidance says generative AI should not directly create factual journalism, then includes this exception: "unless it is the subject of the content and its use is illustrative." BBC Editorial Guidelines, guidance on the use of artificial intelligence, retrieved 27 August 2026 The clause in context, on bbc.co.uk. Screenshot taken 27 August 2026. This film is about AI and uses AI as illustration, so that exception appears relevant on its wording. It is not the whole rule. The adjacent paragraphs also require that AI media not challenge the editorial meaning of the content, distort the meaning of events, alter the impact of genuine material, or otherwise materially mislead audiences. A separate section requires staff proposals to be referred to a senior editorial figure, who should consult Editorial Policy. I cannot see that internal process. The public evidence therefore establishes neither full compliance nor a breach. ## Finding 1: the BBC's per-shot AI caption stops at 05:22 The BBC's per-shot "Generated with" caption is not applied to the whole of the AI 2027 film. The last captioned shot is at roughly 05:22. I sampled the caption region every ten seconds from 05:22 to 08:06. The caption appears at 05:22 and in none of the sixteen later samples. I then sampled every two seconds across 07:04 to 07:42, the densest patch of generated footage in the back half. Absent in 20 of 20 frames. Caption coverage across the runtime, sampled from the published video on 27 August 2026. Hand-authored graphic, not generated imagery. Both markers die on the same shot. The last segment carrying the tool caption, 05:17 to 05:23, is also the last one carrying a year stamp, and the frame sample that still shows the caption is at 05:22. After that shot, neither marker returns. About 35 seconds of uncaptioned generated footage runs after that point: roughly 05:46 to 05:53, then 07:05 to 07:09, 07:09 to 07:16, 07:16 to 07:25, and 07:29 to 07:37. That is 35 seconds inside a 166 second stretch, not 166 seconds of it. The footage is unmistakably the same synthetic material used earlier: the OpenBrain control room, the corridor run, the lab coat crowd, the OpenBrain building. One shot around 07:12 carries a faint "Veo" watermark bottom right, which is the generator's residual mark rather than the BBC's caption. This is the closing section of the film, the part built around the real interviews. Marcus is on camera until 06:45, Larsen from 06:58, and Altman appears at 07:44. The uncaptioned synthetic shots are cut between them. For five minutes a viewer could tell fabricated from filmed by glancing at the corner. In the section where fabricated and filmed sit closest together, that particular cue is not there. I am not calling this deception. It reads like an editorial decision that captions belong on the scenario sequence and not on the discussion sequence, and a viewer who watched from the start was told at 00:30. The narrower question I would put to any newsroom, mine included: once you have trained an audience to rely on a signal for five minutes, what does removing it teach them? ## Finding 2: the BBC AI 2027 video contains a generated news report inside a real news report At 03:42 the BBC AI 2027 video shows a street protest against OpenBrain, the fictional frontier AI lab at the centre of the AI 2027 scenario, in a shot captioned "Generated with Veo AI". In the foreground a woman delivers a piece to camera holding a microphone with a network logo on the flag. The correspondent is generated, and the network on the mic flag is not one I can identify. Frame at 03:45, captioned "Generated with Veo AI" by the BBC, with a "2028" year stamp top left. Low-resolution excerpt used for criticism and review. Whatever the prompt was, the output reproduced the visual grammar of television news: a correspondent, a mic flag and a brand-like mark. The mic flag is the tell. A blue circular emblem, the word NEWS legible beneath it, and above that, letterforms that are not letters. It is the shape of a news brand without a word in it. Detail of the microphone flag at 03:45, magnified from the 360p source. The placards fail the same way. The clearest reads approximately "OPPEEM / UNOR". Two visually identical hand-drawn "BRAIN" placards appear in the same frame, which is not what a row of separately handmade signs usually looks like. Detail of a placard at 03:45, magnified from the 360p source. By the quality half of the Merriam-Webster definition, this shot has the defect the word names. I will not call a single shot slop outright, because "usually in quantity" is doing real work in that definition. I will say it is the kind of visual defect the definition describes. It was visibly captioned as generated, and it is still a generated news-style scene containing non-words, in a video that had 11,479,084 views when I captured the watch page on 27 August 2026. ## Finding 3: the BBC's year stamp marks when, not whether Here is where I had to correct myself. My first draft said the tool caption was the only label in the film, and that it told you the pixels were synthetic without telling you the event was hypothetical. The first half of that was wrong. There is a second on-screen marker, and it appears on 54 of 89 segments in the model pass: the year stamp. But look at what a year stamp actually communicates. "2028" fixes when the depicted thing sits. It does not say whether it will happen. A caption reading 2028 over photoreal footage of a protest is closer to a dateline than to a hedge. That is my reading of the frame, not an audience-response finding. So the corrected claim, which I think is stronger than the one I started with: the film marks provenance, and it marks time, and it does not mark epistemic status on the shot itself . Precisely: I found no simultaneous shot-level overlay on the generated scenario footage stating that the depicted event is hypothetical. No hedge, scenario label or reconstruction caption appears in any of the 89 segment descriptions or in any frame I read, and I did not run a dedicated frame scan for one the way I did for the tool caption, so treat that as an absence I looked for rather than one I proved. The film does communicate uncertainty, just not on the shot. It puts the forecast distribution on screen at 06:03, shows the two-ending interface at 06:51, and the presenter says the word fictional at 07:37. All three arrive after the scenario has played. That is the gap: the qualification exists, and it is never simultaneous with the footage it qualifies. That gap matters because of what the narration is doing at the same moment. The sixteen attributions above run from 00:08 to 05:16, an average interval of about twenty-one seconds across the scenario sequence. The shot itself does not carry an attribution of the scenario claim. A photoreal image alone does not tell the viewer whether it depicts a prediction, so that status has to be carried by something added to the frame. A practical fix is a simultaneous shot-level label such as "SCENARIO" or "HYPOTHETICAL". I have no audience data on which wording readers understand best. What I can say is that a separate status label is available to add and absent from the generated scenario frames I examined. Pew Research Center, surveying 5,023 US adults with fieldwork 9 to 15 June 2025, found 76% say it is extremely or very important to be able to tell whether pictures, videos and text were made by AI or by people, while 53% are not too or not at all confident they can. That survey is about human versus AI origin, not whether a depicted event is hypothetical, but it shows why visible origin labels matter to readers. ## Finding 4: how the BBC AI 2027 video splits its runtime The AI 2027 authors state on their own site: "We wrote two endings: a 'slowdown' and a 'race' ending." Using the chapter markers the BBC publishes in its own video description: Runtime allocation from the BBC's published chapter markers, measured 27 August 2026. Hand-authored graphic, not generated imagery. Across five published scenario chapters, the scenario runs 00:35 to 05:39, which is 304 seconds, 62.3% of the runtime. The Gary Marcus chapter is 74 seconds and the Thomas Larsen chapter is 75 seconds. That is 4.05 to 1 for the scenario against the alternative ending chapter, and 2.04 to 1 against both counterpoint chapters combined. Both numbers are worth having, and the second is the more generous reading. Two honest caveats on that arithmetic. Chapter lengths are not speaking time: Marcus speaks for roughly 45 of his 74 seconds, and Larsen for roughly 40 of his 75, with presenter narration, screen recordings and b-roll filling the rest. And the two endings share a trunk. The branch point in the paper is the Oversight Committee decision, which the film reaches at about 03:10, so roughly 155 seconds of that 304 belongs to both endings equally. Measured branch to branch, the race branch from 03:10 to 05:39 is 149 seconds against 46 seconds of slowdown material from 06:51 to 07:37. About 3.2 to 1. The descriptive result survives all three measurements: the slowdown branch appears in the final minute, while the race branch receives more screen time under either the chapter or post-branch comparison. One more asymmetry, in the framing rather than the runtime. At 00:15 the narration attributes to the researchers a prediction that humanity would be gone within five years. Footnote 1 of AI 2027, present since publication, says the authors disagree on timelines, describes the scenario as closer to their modal estimate than their later median, and states that their goal is prediction rather than recommendation. The film does show that qualifying material. It arrives at 06:03, five minutes and forty eight seconds after the framing at 00:15, and after the scenario has played out in full. Placement, not omission, is the criticism. On 22 November 2025, roughly four months after the film went out, the authors added this to their front page: The authors added that they "don't know exactly when AGI will be built" and clarified that 2027 was their modal year, while their medians were later. ai-2027.com front page, update dated 22 November 2025, retrieved 27 August 2026. The two-endings statement and the November 2025 clarification, on ai-2027.com. Screenshot taken 27 August 2026. I cannot show the film caused that clarification and I am not claiming it did. What I can say is that the authors amended their front page in a way that directly addresses the timeline interpretation invited by the 00:15 framing. ## The strongest case for what the BBC did The subject of the film is AI. Illustrating it with AI is not decoration, it is demonstration: the audience sees what these tools produce while being told what they are. That can be more informative than generic server-rack footage. These events cannot be documented as real events because they have not happened. The available editorial choices include generated illustration, conventional animation, motion graphics, staged dramatisation, stock footage, or no reconstruction. The BBC used per-shot tool labels rather than relying only on a blanket statement. That is a more granular disclosure choice, and it deserves credit even though the label does not cover every reused generated shot. I still think the film leaves viewers with a stronger impression of inevitability than the paper supports, and the sections above are why. ## Applying Google's disclosure questions as a reporting lens Every rubric is a choice, so here is mine and my reason for it. Google's helpful content guidance asks publishers three questions about AI-generated content. It is written for search rather than for broadcast ethics, which is a real limitation, but it has the advantage of being public, specific and not written by me. The public artifact makes AI use self-evident, provides partial production background, and does not explain why generated video was useful beyond calling it an experiment. BBC World Service "AI2027" viewed through Google Search Central's three AI disclosure questions, 27 August 2026. This is a reporting lens, not a BBC compliance score. Google's question Evidence Assessment Is the use of AI self-evident? Spoken at 00:30, stated in the description, captioned on scenario shots through 05:22 Present Does the publisher explain how AI was used? Five generators named on screen, but no account of prompting, selection, or how much footage was rejected Partial Does the publisher explain why AI was useful? Described once as "an experiment" at 00:30; no further rationale found in the video or description Not found in public artifact ## Why the disclosure still leaves a clipping gap If a clip starts after the spoken disclosure at 00:30 and is separated from the YouTube watch page, the spoken line and description are no longer inside that clip. A burned-in on-screen caption is different because it travels with the pixels. A twenty second excerpt from the scenario sequence before 05:22 therefore carries its own tool label. A twenty second excerpt made from the later reused generated footage does not. The strongest of the three disclosure layers is also the one with incomplete shot coverage. I could not determine whether the source assets carried machine-readable provenance, and I am not asserting either way. ## What I would change if I shipped this I build [AI content pipelines in Claude Code](/blog/claude-code-seo-stack), so I read this as a production spec. Four rules I am adopting. - Label provenance and epistemic status separately. "Generated with Veo AI" says the pixels are synthetic. A year stamp says when. Neither says the event is hypothetical. Add a simultaneous status label such as "SCENARIO" or "HYPOTHETICAL". - Whatever the labelling rule is, hold it to the last frame. Five minutes of reliable captions is a promise to the viewer. Dropping the caption at 05:22 spends the trust the first five minutes built. Reused generated shots should retain the same overlay. - Put the strongest labelling where synthetic sits closest to real. The scenario sequence is captioned through 05:22. The closing section, where generated shots are cut against real interviews, is not. - Check the text in every generated frame. Both visible artifacts I found are text failures: the placards and the mic flag. Both are legible at 360p. Read every sign, logo and screen before the edit ships. Reproduce the caption scan on this 640x360 file with one cropped frame every ten seconds: ``` for t in $(seq 322 10 488); do ffmpeg -ss "$t" -i video.mp4 -frames:v 1 \ -vf "crop=260:34:380:6" "cap_$t.png" done ``` The crop is specific to this source resolution and caption position. Change the geometry for a different video, then inspect the output strip. Same problem, less room to qualify: my [text-side AI slop case study](/blog/claude-code-blog-writer) and [Claude Code skills guide](/blog/best-claude-code-skills-2026) document the related content checks. ## What this analysis does not claim - I am not claiming the BBC complied with or broke its own guidance. The public artifact cannot establish the complete editorial process or the guidance's full application. - I am not claiming viewers were deceived. I have no audience data for this film, only general survey data about attitudes to AI imagery. - I am not claiming intent, cost-cutting, or an absence of editorial review. I have no visibility into any of that. - View counts are not people. 11,479,084 views is an unknown and smaller number of viewers, and the figure drifts upward daily. A note on the assets on this page, because a piece about disclosure should disclose. The two charts are hand-authored SVG rendered to WebP. The two source screenshots are captures of pages I quote, taken 27 August 2026. The frame stills are low-resolution excerpts of the BBC's published video, used for criticism and review, each captioned with its timestamp. The cover title card was generated with ChatGPT's image-generation tool on 27 August 2026, then cropped and exported locally as a 1200x630 WebP. The exact underlying image model is not recorded in the PNG metadata. The audio summary at the top is narrated by a voice model of my own voice, and its background music was generated locally with [ACE-Step in my local music pipeline](/blog/claude-music-ai-production). Arguing this case with undisclosed generated assets would be indefensible, so each asset class is disclosed here. ## FAQ: questions readers ask ### Did the BBC hide that the AI 2027 video was AI generated? No. It disclosed three ways: spoken at 00:30, in the YouTube description, and via per-shot on-screen captions naming five different generators, Sora, Runway, Kling, Hailuo and Veo. ### Did the BBC break its own AI rules? The public evidence does not establish compliance or a breach. A subject-and-illustration exception appears relevant, but adjacent conditions address editorial meaning, material misleading, and internal referral. I cannot observe that internal process from the film. ### So what is the actual problem with the video? The film marks provenance with a tool caption and time with a year stamp. It later shows a forecast distribution, two endings, and calls the scenarios fictional. I found no simultaneous shot-level overlay marking the depicted event as hypothetical, and the tool caption stops before later generated shots. ### Is the BBC AI 2027 video AI slop? I do not classify the full film as AI slop. Individual generated frames contain the kind of low-quality defects the term describes, including non-lexical placard text and an unreadable network-style mark. The film also discloses AI use three ways and includes a named critic. ### Where does the BBC's disclosure caption stop? At roughly 05:22. Sampling the caption region every ten seconds from 05:22 to 08:06 found it present at 05:22 and absent in all sixteen later samples. About 35 seconds of generated footage appears after that point. ## Sources - BBC World Service, "AI2027: Is this how AI might destroy humanity?", published 1 August 2025. [Watch page](https://www.youtube.com/watch?v=1UufaK3pQMg). View count 11,479,084 and chapter markers captured 27 August 2026; the watch page HTML is archived. - [BBC Editorial Guidelines, guidance on the use of artificial intelligence](https://www.bbc.co.uk/editorialguidelines/guidance/use-of-artificial-intelligence), retrieved and archived 27 August 2026. - [AI 2027](https://ai-2027.com/), published 3 April 2025 by the AI Futures Project. Front page and footnotes retrieved and archived 27 August 2026. - [Merriam-Webster entry for "slop"](https://www.merriam-webster.com/dictionary/slop), sense 1a, retrieved and archived 27 August 2026. - [Google Search Central, "Creating helpful, reliable, people-first content"](https://developers.google.com/search/docs/fundamentals/creating-helpful-content), retrieved and archived 27 August 2026. - [Pew Research Center, "How Americans View AI and Its Impact on People and Society"](https://www.pewresearch.org/science/2025/09/17/how-americans-view-ai-and-its-impact-on-people-and-society/), published 17 September 2025. 5,023 US adults, fieldwork 9 to 15 June 2025. ## Related reading - [How I test written content for AI slop](/blog/claude-code-blog-writer) - [The local AI music pipeline behind this audio edition](/blog/claude-music-ai-production) - [The inspectable AI marketing stack I use daily](/blog/ai-marketing-automation-stack) *** # YouTube Pro: One Evidence Chain From Research to Thumbnail - URL: [https://agricidaniel.com/blog/youtube-pro-research-to-thumbnail](https://agricidaniel.com/blog/youtube-pro-research-to-thumbnail) - Published: 2026-08-27 - Updated: 2026-08-27 - Category: Open Source - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Meet YouTube Pro, an open-source local-first workflow for public YouTube research, grounded AI insights, script writing, and thumbnail creation. YouTube Pro is an open-source, local-first workspace that carries one public YouTube research snapshot through AI Insights, grounded ideas, an editable script, and a thumbnail. It is built for creators who want fewer context switches and a clearer line between what the public data shows, what AI infers, and what only YouTube Studio can validate. It is the public, rebuilt successor to the browser-driven workflow I documented in [my original YouTube keyword research experiment](/blog/automate-youtube-keyword-research-claude). I built YouTube Pro, so this is a first-party product guide, not an independent review. The code is public under the Apache License 2.0, and the [v1.0.0 source is available on GitHub](https://github.com/AgriciDaniel/youtubepro/releases/tag/v1.0.0). ## Watch the complete creator walkthrough The launch video shows the real product flow from research to thumbnail in 7 minutes 31 seconds. It also explains the context-loss problem that led me to build the app. ▶ Click to load the privacy-enhanced YouTube player 7:31 [](https://www.youtube.com/watch?v=fzobKIjUN_E) Video: “I built an AI YouTube tool and open-sourced it” by Agrici Daniel. The privacy-enhanced YouTube player loads only after you activate it. [Watch directly on YouTube](https://www.youtube.com/watch?v=fzobKIjUN_E). The walkthrough covers the origin, the connected stages, the evidence labels, local workflow history, the script teleprompter, Thumbnail Creator, and the public repository. If you prefer to inspect before installing, start here and keep the GitHub source open beside it. ## Listen to the audio edition This site edition includes a concise narrated summary in Daniel's authorized local 02-warm voice, with an original sample-free music bed mixed beneath the speech. It includes downloadable MP3 audio, English WebVTT captions, and a visible synchronized transcript. Listen instead ### YouTube Pro in one evidence chain 3:01 Your browser does not support HTML audio. Use the MP3 download below. AI-generated audio using Daniel's authorized local 02-warm voice at 1.05 times speech speed. The original sample-free music bed is sidechain-ducked beneath the narration. Duration: 3:01. [Download MP3](/audio/youtube-pro-audio-summary.mp3)[Download English captions](/audio/youtube-pro-audio-summary.vtt) Read the synchronized transcript 0:00 YouTube Pro is an open-source workspace I built to keep one YouTube project connected from research to thumbnail. 0:06 The reason is simple. 0:08 Research kept losing its context. 0:10 I would compare videos in one place, take notes somewhere else, ask AI for ideas in another window, then rebuild the whole argument when it was time to write the script and design the thumbnail. 0:22 By the end, the creative decision was still there, but the evidence behind it had quietly wandered off. 0:28 YouTube Pro keeps that chain together. 0:30 First, the research screen gathers and compares as many as fifty public YouTube videos. 0:35 It can show titles, publication dates, duration, views, visible interactions, tags, and channel information. 0:43 It also shows clear warnings when public fields are missing. 0:47 Next, AI Insights works from that exact snapshot. 0:51 The brief separates three things. 0:53 Observed means the claim is directly present in the public data or calculated from it. 0:57 Inferred means it is a useful hypothesis, not a conclusion. 1:01 The third label is Requires Studio. 1:04 It means the answer needs private channel analytics or a real publishing test. 1:08 That distinction matters. 1:10 Public views can help you compare a sample. 1:12 They cannot reveal impressions, click-through rate, retention, traffic sources, revenue, viewer satisfaction, or the mysterious future mood of the recommendation system. 1:21 Apparently, the algorithm declined my request for a crystal ball. 1:24 After Insights, the same evidence becomes grounded video ideas. 1:29 You choose one, then carry it into the Script Writer. 1:32 The script stays editable, individual sections can be regenerated inside the same context, and the teleprompter gives you a focused reading surface. 1:41 The Thumbnail Creator finishes the chain. 1:43 It starts from the selected promise and brief, supports a small set of permitted references, and produces a sixteen by nine image you can revise and test. 1:54 A generated thumbnail is still a hypothesis. 1:54 YouTube Studio is where packaging performance gets measured. 1:57 The application runs locally and binds to your loopback address by default. 2:01 Your eight most recent workflows stay in the current browser profile. 2:05 Research calls the YouTube Data API, while insights, ideas, scripts, and thumbnails call Gemini. 2:13 Local-first means control of the workspace. 2:15 It does not mean the network has taken the day off. 2:18 The public version one release is available under the Apache License, version two point zero. 2:23 For this guide, the pinned release passed sixty-two automated tests, TypeScript checking, client and server builds, and a high-severity dependency audit with zero reported vulnerabilities. 2:34 That is evidence of a tested release, not a promise that software has achieved spiritual perfection. 2:40 If you want to try it, start with one narrow topic. 2:43 Inspect the source rows in the result. 2:45 Challenge one recommendation. 2:47 Keep an idea only when you can explain which evidence supports it and what YouTube Studio still needs to validate. 2:53 That is the point of YouTube Pro. 2:56 It does not automate judgment. 2:58 It keeps the receipts attached while you use it. ## The short version YouTube Pro gives you one connected project for five jobs: - Search and compare up to 50 public videos. - Generate AI Insights from that exact snapshot. - Turn the evidence into grounded video ideas. - Write and edit a script, then read it in a teleprompter. - Build a 16:9 thumbnail from the selected promise and brief. The important word is connected . Your research does not disappear when you open the script writer. The selected idea, evidence context, script, thumbnail brief, and generated result stay together in a recent local workflow. The equally important boundary is public . YouTube Pro can inspect returned public metadata and calculate useful proxies. It cannot see your impressions, click-through rate, retention curve, traffic sources, revenue, or viewer satisfaction. Those require owner-authorized analytics or a real publishing test. ### What you will see in this guide - [Why I built one workflow instead of another isolated AI tool](#why-i-built-it) - [How the five stages fit together](#workflow) - [What the research layer can actually measure](#public-data) - [How the evidence labels resist confident nonsense](#evidence-labels) - [How ideas, scripts, and thumbnails keep their context](#creation) - [What YouTube Pro deliberately does not claim](#limits) - [How local-first storage and API keys work](#local-first) - [How to run the public release](#setup) - [The full 7 minute 31 second walkthrough](#walkthrough) - [Who should use it](#fit) - [Release verification and open-source next steps](#open-source) ## Why I built it: good research kept losing its context The problem was not a lack of tools. It was the handoff between them. Research happened in YouTube tabs. Notes lived somewhere else. An AI chat produced an idea without retaining the exact source set. The script moved into another editor. Thumbnail prompts began from a blank box. By the time the package was ready, it was hard to answer a basic question: which evidence led to this promise? That friction shaped the product. YouTube Pro treats a video project as a continuous evidence trail, not five unrelated generators. A search creates a named snapshot. Insights analyze that snapshot. Ideas inherit it. The selected idea becomes the script input. The same promise and thumbnail concept continue into packaging. This does not make creative judgment automatic. It makes the chain inspectable. You can still disagree with the model, revise the idea, rewrite a section, or start a new workflow. The product's job is to preserve the reasoning context while you do that. ## The five-stage workflow at a glance Figure 1. One active research snapshot flows into ideas, the script, and the final thumbnail brief. Full text alternative: Research gathers up to 50 public videos. AI Insights labels observed facts, inferences, and Studio-only needs. Grounded Ideas turn that evidence into testable angles. Script Writer creates and edits the selected idea. Thumbnail Creator packages the selected promise. ### 1. Research Enter a topic, choose filters, and inspect the returned public-video sample. You get overview metrics, publication and duration patterns, momentum proxies, data-coverage warnings, and the source rows used by the analysis. ### 2. AI Insights Gemini receives the active ordered snapshot and its deterministic aggregates. The output is a scan-first brief with audience questions, opportunity hypotheses, recommended experiments, and an expandable evidence ledger. ### 3. Grounded Ideas Ideas generate after valid Insights. Each one starts from the research package instead of a blank prompt. You select the idea that best matches the intended viewer and honest promise, then explicitly continue to Script Writer. ### 4. Script Writer The selected idea becomes an editable script. You can regenerate a section or paragraph within the same bounded evidence context, then switch to a focused teleprompter with pace, size, cue, undo, and playback controls. ### 5. Thumbnail Creator The selected promise and thumbnail concept become a practical brief. You can use outcome-oriented presets, adjust controls, add up to three permitted reference images, generate a readable 16:9 image, and create variations. There is no hidden standalone Ideas product between Research and Script Writer. The current [repository workflow contract](https://github.com/AgriciDaniel/youtubepro/blob/2bacd513c91c8b9cfbe4ae2a4ac4e834ee6dc19a/README.md#workflow) documents the same five-stage path. ## Research starts with a public-data snapshot, not a prediction YouTube Pro uses the YouTube Data API v3 to search for videos and enrich the returned IDs with public video and channel fields. Depending on availability, that can include titles, descriptions, tags, publication time, duration, views, public likes and comments, channel metadata, caption presence, topic categories, and selected status fields. Figure 2. The Research overview in a live local development build. The interface presents returned public metadata, deterministic summaries, coverage, and the active source sample. The dashboard then builds deterministic views that help you read the sample without letting one viral result dominate: - Median and average views appear together because large outliers can distort an average. - Views per day normalizes public views by video age. It is a momentum proxy, not real-time velocity. - Visible interaction rate uses public likes plus comments divided by views for complete rows. It is not a complete engagement or satisfaction metric. - Duration, recency, tags, and channel diversity expose patterns in the returned supply. - Coverage indicators show where public fields are unavailable instead of silently replacing missing values with zero. That last behavior matters. A hidden subscriber count and a real zero are not the same fact. Honest analysis preserves the difference. The sample is also not a census. YouTube Pro accepts between 1 and 50 returned videos per request. The provider's overall result count is approximate, and search behavior can vary with query settings and context. The tool can help you inspect a bounded public snapshot. It cannot convert that snapshot into a universal claim about an entire market. The [official YouTube Data API overview](https://developers.google.com/youtube/v3/getting-started) explains the API and key requirement. The [video resource documentation](https://developers.google.com/youtube/v3/docs/videos) shows why some fields are public while others require authorization. YouTube Pro keeps that provider boundary visible in the product instead of smoothing it away in the copy. ## Three evidence labels keep the brief honest AI can make a weak pattern sound final. YouTube Pro addresses that failure mode with three explicit evidence classes. Figure 3. The evidence boundary. Full text alternative: Observed means directly present or deterministically calculated from the active public snapshot. Inferred means a useful interpretation that must remain a hypothesis. Requires Studio means the answer depends on owner-authorized analytics or a controlled publishing test. ### Observed An observed statement must be directly present in the snapshot or deterministically calculated from it. Source-specific claims retain source video IDs. Snapshot identity travels with the request so an answer cannot quietly cite a different research set. ### Inferred An inferred statement interprets observed metadata. For example, repeated title structures may suggest a packaging pattern. That can be useful, but it remains a hypothesis. Repetition in the public supply does not prove audience demand. ### Requires Studio Some decisions need channel-owner data: impressions, click-through rate, watch time, average view duration, retention, traffic sources, returning viewers, revenue, or private audience dimensions. The [YouTube Analytics reporting documentation](https://developers.google.com/youtube/analytics/channel_reports) describes those owner-authorized reports. YouTube Pro names the missing metric instead of inventing it. When the evidence is absent, the correct output is insufficient evidence. That sounds less magical than a prediction. It is also far more useful when your next action involves weeks of production work. Figure 4. AI Insights keeps the research brief, evidence balance, and ledger in one scan-first view. This schema is a guardrail, not a guarantee of model accuracy. You should still inspect the source rows, challenge an inference, and validate owner-only claims after publishing. The benefit is that the interface gives you somewhere concrete to do that. ## The evidence chain continues into ideas, scripts, and thumbnails Many research tools end with a dashboard. Many AI writing tools begin with a blank prompt. YouTube Pro connects those two moments. After Insights finishes, the app generates grounded ideas from the same research package. A useful idea should name a viewer, an honest outcome, a likely discovery surface, and a packaging direction. You select one before moving forward. That explicit choice is important because a model can offer options, but it should not take over your editorial decision. The Script Writer then receives the selected idea and its evidence. You can edit the output directly, regenerate a bounded section or paragraph, and preserve your revisions. The teleprompter turns the final draft into a reading surface without requiring another export. Figure 5. The editable script and focused teleprompter stay inside the same saved workflow. The Thumbnail Creator completes the package. It starts from the selected promise and thumbnail concept instead of asking you to reconstruct the idea from memory. Optional references are limited and validated, and the browser intentionally does not retain uploaded reference files in workflow history. That means permission and file selection are fresh when you generate again later. Figure 6. Thumbnail Creator uses the project's promise and brief while keeping creation controls editable. Gemini image outputs include Google's invisible SynthID provenance, according to the [official Gemini image-generation documentation](https://ai.google.dev/gemini-api/docs/image-generation). YouTube Pro does not add a visible watermark or claim that SynthID can be disabled. Packaging still needs a real test. YouTube's native title and thumbnail experiments can compare up to three options and select a winner using watch time, not click-through rate alone. The [official YouTube test guide](https://support.google.com/youtube/answer/16391400) is the right place to confirm the current behavior before planning an experiment. ## What YouTube Pro deliberately does not claim The cleanest way to understand the product is to see its boundaries beside its capabilities. YouTube Pro can YouTube Pro cannot Search and enrich a returned sample of public videos Measure YouTube search volume Compare public views, age, duration, recency, tags, and visible interactions Predict the recommendation algorithm or a viral result Calculate age-normalized and coverage-aware proxies Observe impressions, CTR, retention, watch time, or traffic sources without owner authorization Generate hypotheses tied to one active snapshot Turn a public sample into a market census Carry research context into ideas, scripts, and thumbnails Replace creator judgment, rights review, or post-publication measurement Store recent workflow state in the current browser profile Sync projects to another device or act as a hosted team service There is another subtle boundary: public thumbnail URLs do not mean the research model inspected thumbnail pixels. The Research stage evaluates public metadata and patterns. Visual packaging review still needs image analysis or a human eye. These constraints are not footnotes to hide below the call to action. They define what a responsible research recommendation can be. The project's [YouTube Research Playbook](https://github.com/AgriciDaniel/youtubepro/blob/2bacd513c91c8b9cfbe4ae2a4ac4e834ee6dc19a/docs/YOUTUBE_RESEARCH_PLAYBOOK.md) documents the same separation between public evidence, hypotheses, and owner-only validation. ## Local-first means control of the workflow, not zero network traffic YouTube Pro runs as a local Node application and binds to 127.0.0.1:5000 by default. There is no product login gate. Your eight most recent workflows live in IndexedDB in the current browser profile. API keys stay in the server environment, and saved key values are not returned to the browser. That design reduces unnecessary product infrastructure, but it is not an offline claim: - Research sends requests to the YouTube Data API. - Insights, ideas, scripts, and thumbnails send bounded requests to Gemini. - A Google account and provider keys may be needed to use those services. - Provider quotas, terms, pricing, and availability still apply. The app also avoids retaining thumbnail reference uploads in workflow history. Research snapshots, ideas, scripts, briefs, and generated results can be restored. The original reference files must be selected again for a later generation. The local Settings endpoint accepts direct loopback, same-origin requests and rejects normal forwarded or reverse-proxy requests. Billable routes have an in-memory, per-process rate limiter. Those controls match a single-user local default. They do not turn the app into a hardened internet service. Do not expose it directly to the public internet. The [security policy](https://github.com/AgriciDaniel/youtubepro/blob/2bacd513c91c8b9cfbe4ae2a4ac4e834ee6dc19a/SECURITY.md) calls for authentication, trusted secret management, shared rate limiting, safe observability, and a deployment-specific threat review before remote use. ## Run the public release locally You need Node.js 20.19 or newer, a YouTube Data API v3 key for Research, and a Gemini API key for the generative stages. The current repository also supports the relevant Node 22 toolchain noted in its README. If provider credentials are new to you, [my Google API automation guide](/blog/google-api-seo-automation-claude-code) explains the broader setup pattern. ``` git clone https://github.com/AgriciDaniel/youtubepro.git cd youtubepro cp .env.example .env npm install npm run dev ``` Add the two keys to your local .env , or start the app and enter them through Settings. Then open http://127.0.0.1:5000 . For a safer first session: - Start with a narrow topic and a sample small enough to inspect manually. - Read the coverage notes before the AI brief. - Open several source rows and challenge one recommendation. - Choose an idea only after you can state its viewer and promise in one sentence. - Edit the script in your own voice. - Treat the thumbnail as a hypothesis to test after publishing. Open source does not mean provider usage has no cost. Check the current [YouTube quota documentation](https://developers.google.com/youtube/v3/determine_quota_cost) and [Gemini pricing](https://ai.google.dev/pricing) for your account before running high-volume workflows. ## Who YouTube Pro is for, and who should skip it YouTube Pro is a strong fit if you: - Want public competitor and topic research to remain connected to production. - Prefer transparent evidence labels over a confident black-box score. - Are comfortable running a local Node application and supplying your own API keys. - Want editable scripts and thumbnails, not one-click publishing. - Value open source, inspectable boundaries, and browser-local project history. It is probably the wrong fit if you: - Need a hosted multi-user workspace with accounts, permissions, and cloud sync. - Expect private channel analytics without connecting an owner-authorized account. - Need a guaranteed performance score or automatic publishing decision. - Cannot review provider terms, quotas, rights, and generated outputs yourself. - Want an offline-only application with no external API requests. That distinction is intentional. YouTube Pro is a focused local creator workspace, not a hidden SaaS, an analytics replacement, or an automatic channel operator. ## Public release, verification, and the open-source path The public v1.0.0 release is pinned to commit 2bacd513c91c8b9cfbe4ae2a4ac4e834ee6dc19a and licensed under Apache-2.0. The release includes the complete workflow, current product screenshots, security guidance, contributor instructions, and automated checks. The release follows the inspectable, source-first distribution approach I explain in [my open-source growth case study](/blog/how-i-got-8000-github-stars). For this article package, I cloned the pinned commit into a separate clean checkout, installed its locked dependencies, and ran npm test , npm run check , npm run build , and an npm audit at the high severity threshold on 2026-08-26. The result was 62 passing tests, a passing TypeScript check, completed client and server builds, and zero reported audit vulnerabilities. The local environment was Node 24.16.0 with npm 11.13.0 on 64-bit Linux. The build also reported a large client-chunk warning and stale Browserslist data, so the result is a completed build, not a claim of perfect optimization. I separately verified the [successful public CI run at the exact release commit](https://github.com/AgriciDaniel/youtubepro/actions/runs/32784869560). That workflow repeats install, tests, type checking, and the production build on Node 20.19.0. This gives the release a second, public execution record without pretending that passing checks make the application bug-free. You can inspect the [source, issue tracker, release, contribution guide, and security policy on GitHub](https://github.com/AgriciDaniel/youtubepro). If the evidence model or local workflow is useful to you, the best next step is not to trust this article. Open the code, read the boundaries, run a small research project, and try to refute one recommendation. Inspect before you trust ## Run one topic through the full chain The source is public, the boundaries are documented, and the walkthrough takes 7 minutes 31 seconds. [Explore the source](https://github.com/AgriciDaniel/youtubepro) [Watch the walkthrough](#walkthrough) ## Frequently asked questions ### Is YouTube Pro affiliated with YouTube or Google? No. It is an independent open-source project. YouTube and Google product names are trademarks of their respective owners. ### Does YouTube Pro use private YouTube Studio data? No. The current Research workflow uses public YouTube Data API metadata. It labels conclusions that need impressions, CTR, retention, traffic sources, revenue, or other owner-only analytics as requiring Studio. ### Can it tell me which video will go viral? No. It can compare a returned public sample and generate testable hypotheses. It cannot predict YouTube's recommendation system, viewer satisfaction, or future performance. ### Is the application fully offline? No. The app and recent workflow history run locally, while Research calls YouTube and generative features call Gemini. Local-first describes the application and storage model, not an absence of network requests. ### Where are my API keys stored? Keys stay in the local server environment. If you save them through Settings, the server writes replacements to the ignored .env file with owner-only permissions and does not return saved values to browser state. ### Where are recent projects stored? The eight most recent workflows are stored in IndexedDB in the current browser profile. They are not synchronized to another browser or device. Uploaded thumbnail references are intentionally not retained. ### Do I need both API keys? Research needs a YouTube Data API v3 key. AI Insights, ideas, scripts, and thumbnails need a Gemini API key. You can start the interface without keys and add them locally in Settings. ### Can I host it for a team? Not safely as-is. The current release is designed for loopback, single-user local use. A remote deployment needs authentication, a trusted secrets path, shared rate limiting, safe observability, and a dedicated threat review. ### What license does the project use? Apache License 2.0. Read the repository's license and notices before redistributing or building a derivative. ## Related reading - [How I automated YouTube keyword research with one browser agent prompt](/blog/automate-youtube-keyword-research-claude), the earlier experiment that led to this rebuilt public tool. - [Google API SEO automation with Claude Code](/blog/google-api-seo-automation-claude-code), a practical companion for provider credentials and source data. - [How I reached 8,000 GitHub stars](/blog/how-i-got-8000-github-stars), the open-source distribution principles behind releases like YouTube Pro. - [The open-source AI marketing automation stack I use daily](/blog/ai-marketing-automation-stack), the wider system this creator workflow belongs to. ## Keep the evidence attached to the creative decision A research tool should not make uncertainty disappear. It should show you what is known, what is inferred, and what experiment closes the gap. That is the principle behind YouTube Pro. One public snapshot becomes an inspectable brief. One selected idea becomes an editable script. One honest promise becomes a thumbnail to test. The workflow stays together, and the boundary between public evidence and private performance remains visible. [Explore YouTube Pro on GitHub](https://github.com/AgriciDaniel/youtubepro) , watch the walkthrough, and run one small topic through the full chain. Keep the idea only if you can trace it back to evidence and state what YouTube Studio must validate next. ### About the author Agrici Daniel built and maintains YouTube Pro. He develops open-source systems for research, SEO, advertising, and content workflows through the AI Marketing Hub community. This article documents his own product and links to the public source so readers can inspect the claims directly. *** # DeepSeek Harness Explained: 181K Stars, and the Brain I Built to Vet It - URL: [https://agricidaniel.com/blog/deepseek-harness-plain-english-guide](https://agricidaniel.com/blog/deepseek-harness-plain-english-guide) - Published: 2026-08-22 - Updated: 2026-08-22 - Category: Open Source - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. DeepSeek Harness is a 181K-star open-source agent framework where everything is a plugin. I built an evidence-gated Obsidian brain to review its architecture before porting any of it, here's how both work. DeepSeek Harness ( dsh ) is DeepSeek AI's open-source agent harness, and as of this week it sits at 181,048 stars and 19,792 forks on GitHub ([deepseek-ai/deepseek-harness](https://github.com/deepseek-ai/deepseek-harness), checked August 2026). That is not a typo. It is one of the fastest-growing agent frameworks in the open-source ecosystem, and its core idea is almost defiantly simple: everything is a plugin . The framework underneath it is Cordis, and under Cordis there is no privileged core to patch: the model adapter, the tool registry, the session log, and the agent loop itself are all plugins, so every part of dsh is replaceable from configuration instead of a fork. A running dsh instance is a plugin tree assembled at boot from named profiles, such as web and headless , that stack distributable bundles like dsh-base , dsh-web-app , and dsh-headless , then layer a user's own cordis.patch.yml on top. You can print the exact tree your machine boots with dsh --profile web --dump-config , and replace any row in it with a patch of your own. Inside that tree, work moves through a turn/step model: a turn opens when input is claimed and closes once nothing is owed, a step is one model request plus the tools it calls, and durable session events record every step so the log, not memory, is what gets replayed into the next model request. That rule, model-visible means logged , is enforced as a runtime invariant, not a convention. Before I ported a single pattern from it into anything I ship, I wanted an honest map of how it works, not a marketing summary. So I built [DeepSeek Harness Brain](https://github.com/AgriciDaniel/deepseek-harness-brain), an independent, evidence-gated Obsidian knowledge base that reviews the framework's architecture, operations, and security boundaries instead of restating its docs. This post covers both: what DeepSeek Harness is under the hood, and what the brain found worth trusting, porting, or waiting on. Listen to the summary Your browser does not support the audio element. Key Takeaways - DeepSeek Harness has 181,048 stars and 19,792 forks on GitHub as of August 2026, and is still in developer preview with breaking changes expected - Its architecture is built entirely on Cordis, a plugin composability engine: sessions, tools, permissions, and providers are separate, swappable capabilities, not one tangled runtime - DeepSeek Harness Brain is a separate, independent v0.2.0 community release: a source-cited Obsidian vault that reviews the harness instead of summarizing its README - Every claim in the brain is gated against a pinned upstream commit; it currently passes 4 verified evidence lanes and is explicit about 7 runtime tiers it has not yet verified - Five harness patterns are strong, framework-neutral candidates to port into your own agent projects today, independent of DeepSeek Harness or Cordis ## What DeepSeek Harness Is Strip away the branding and DeepSeek Harness is an agent runtime where the loader, not the application, owns composition. Instead of a monolithic core with a hardcoded set of tools and providers, DeepSeek Harness treats sessions, tool execution, permissions, persistence, and model providers as independent plugins that Cordis loads, wires, and can hot-swap. That design is described formally in [A Programming Paradigm for Spatiotemporal Composability](https://github.com/cordiverse/paper), the paper behind Cordis. The unit that makes this concrete is what the docs call a capability seam : a swappable capability with three roles, a Service Definition that declares the interface, a Service Provider that implements it, and a Consumer, usually a model-facing tool, that uses it. Filesystem and subprocess providers share one execution world in dsh , so pointing them at a remote sandbox moves Bash, PTY, and LSP with them, with no provider forks needed elsewhere. Subagent providers vary just as widely behind one interface, from a fresh child agent to a delegated turn in another product entirely. Extension points themselves fall into three domains: durable session events for anything that must survive a reload, live agent/* events for observing or intercepting work in flight, and capability events like fs/* and tools/* for attaching policy without importing the agent loop. You can be running the Web UI in under a minute: ``` npx @deepseek-ai/dsh web ``` That starts a local server at http://127.0.0.1:3080 . Running from source instead gives you the full development loop: ``` git clone https://github.com/deepseek-ai/deepseek-harness.git cd deepseek-harness pnpm install pnpm run build pnpm dsh web ``` The project is upfront that this is a developer preview : package names, defaults, event semantics, and on-disk formats are all still moving. That is not a criticism, it is the honest cost of an everything-is-a-plugin design iterating in public. It does mean anything you build against it today needs to expect churn. Capability seams: definition, provider, and consumer stay separate all the way down ## The Brain I Built to Review It Most "second brain" projects for a piece of software are a reorganized copy of its documentation. I did not want that, mostly because I have been burned by AI-generated technical summaries that state framework behavior with total confidence and no source. So DeepSeek Harness Brain works differently: it is advisory, read-only, and evidence-gated by construction . Every domain claim needs a dated, trustworthy source. Nothing gets a stronger maturity label than the check that produced it. Version 0.2.0 shipped this week as the first community release, reviewed against a pinned upstream commit of DeepSeek Harness. It ships: - A plain-language map of how the harness's subsystems fit together - Source-cited notes on sessions, tools, permissions, persistence, plugins, Cordis, subagents, prompts, providers, and verification - A portability matrix separating reusable patterns from Cordis-specific machinery - A deterministic sample Obsidian vault and an allowlisted checkout importer - A grounded secretary agent plus four curator agents for evidence-based review Getting a working copy running takes about five minutes: ``` python -m pip install -e . deepseek-harness-brain demo deepseek-harness-brain lint --vault examples/sample-vault deepseek-harness-brain report --vault examples/sample-vault --html-only ``` To check a real local DeepSeek Harness checkout against the reviewed evidence instead of the bundled sample: ``` deepseek-harness-brain import-checkout \ --checkout /path/to/deepseek-harness \ --vault examples/sample-vault \ --expected-commit b150a551b8d465e31e418e1b2eaf5e79bbb7d28e \ --as-of 2026-08-21 ``` ## What "Evidence-Gated" Means Here The brain will not let itself claim more than it has checked, and it says so out loud. That is the part I think is worth copying into any technical documentation project, AI-assisted or not. Release 0.2.0 Evidence Ledger Verified (4) - ✓ Allowlisted source capture and domain adapter tests - ✓ Deterministic template and sample vault checks - ✓ Local pipeline and community packaging gate - ✓ License, attribution, secret, local-path, symlink, and archive checks Not Yet Verified (7) - ○ Interactive Web profile - ○ Live DeepSeek provider behavior - ○ macOS, Windows, and ARM - ○ Native and Python release packaging - ○ Comprehensive independent security audit - ○ Complete repository test and release matrix - ○ Production load and long-running durability 4 of 11 evidence tiers verified so far, per [references/release-status.json](https://github.com/AgriciDaniel/deepseek-harness-brain/blob/main/references/release-status.json). Market-ready stays false until the rest close. That is why the project calls itself community-ready and not market-ready : the source, license, and packaging gates pass, but browser, live-provider, native, cross-platform, and production-scale evidence are still open. The release status file says so explicitly instead of rounding up. Evidence enters through pinned source capture; deterministic checks gate every downstream claim ## What to Port First The brain's portability matrix is blunt about which parts of DeepSeek Harness are genuinely framework-neutral versus which only make sense if you have adopted the full Cordis runtime. The strongest candidates for porting into an existing agent project, independent of Cordis entirely, are: - Append-only session events and reconstructable model requests - One guarded tool executor with typed approval and cancellation - Capability seams that separate definition, provider, and consumer - Deterministic prompt and tool-schema assembly - A small durable persistence contract before reaching for a complex backend Profiles, hot reload, model-authored workflows, generated RPC, and the full Cordis runtime are only worth adopting when a product's complexity matches them. Most agent projects do not need the whole harness to benefit from these five patterns. ## Who This Is For - Developers evaluating whether DeepSeek Harness's architecture fits a real product, not a demo - Teams deciding which harness patterns to port into an existing agent stack instead of adopting Cordis wholesale - Operators who want an evidence trail behind every architectural claim instead of an unsourced summary - Contributors who want to check DeepSeek Harness's claims, tests, lifecycle behavior, or security boundaries before relying on them ## Try Both Yourself DeepSeek Harness itself: npx @deepseek-ai/dsh web , or clone and build from source as shown above. It is MIT licensed, actively iterating, and has a [Discord community](https://discord.gg/Ycq5dCaS4) plus [GitHub Discussions](https://github.com/deepseek-ai/deepseek-harness/discussions) for feedback and bug reports. DeepSeek Harness Brain: python -m pip install -e . then deepseek-harness-brain demo . It is also MIT licensed, open source, and the [full source and evidence ledger are on GitHub](https://github.com/AgriciDaniel/deepseek-harness-brain). One disclosure worth being explicit about: DeepSeek Harness Brain is an independent research project. It is not affiliated with or endorsed by DeepSeek. The DeepSeek name and whale mark appear only to identify the framework under review. ## Related Posts - [Claude Obsidian: An AI Second Brain That Actually Cites Its Sources](/blog/claude-obsidian-ai-second-brain) - the sibling approach to evidence-gated knowledge bases, built for Claude Code instead of DeepSeek Harness - [Inside the Claude and Codex Skills Ecosystem](/blog/claude-codex-skills-ecosystem) - how portable agent skills compare across harnesses - [How I Got 8,000 GitHub Stars on an Open-Source AI Tool](/blog/how-i-got-8000-github-stars) - what moves the needle for a new open-source release Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # How I Automate YouTube Keyword Research With One Claude Prompt - URL: [https://agricidaniel.com/blog/automate-youtube-keyword-research-claude](https://agricidaniel.com/blog/automate-youtube-keyword-research-claude) - Published: 2026-08-18 - Updated: 2026-08-18 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I gave Claude one prompt and browser control. It drove my YouTube Data API app, researched SEO, AI SEO, and GEO, and handed me a clean CSV. Zero mouse touches, and the whole setup is reproducible without my app. On August 18, 2026 I gave Claude one prompt, handed it my Chrome tab, and it ran my entire YouTube keyword research: three keywords (SEO, AI SEO, GEO), every metric my dashboard exposes, and one clean CSV that ended up in Google Sheets. I did not touch the mouse once. The model was Sonnet 5 on high reasoning, driving a real browser through Claude's browser control, against a YouTube Data API app I built on Replit about a year ago and then forgot existed. This is the kind of task I used to block an afternoon for. Search a keyword, screenshot the results, tab over to a spreadsheet, repeat until my eyes glaze over. Now the workflow is: write one sentence, go make coffee, come back to a spreadsheet. This post is the full setup - the app, the exact prompt, what the data actually said about the GEO hype, and how to build the same thing yourself without my app. ## One Prompt, Three Keywords, Zero Mouse Touches To automate YouTube keyword research end to end, you need two things: a data source (the YouTube Data API) and an agent that can operate a browser (Claude with browser control). I typed one prompt into the Claude sidebar in Chrome, and here is everything it did on its own: - Opened my YouTube Research Pro app in the active tab - Typed each keyword into the search field and pressed enter - SEO first, then AI SEO, then GEO - Waited for the YouTube Data API results and the AI insights to render, then captured the page - Repeated for all keywords, including the ideas section I did not even ask about - Compiled everything into one well-formatted CSV, ready for Google Sheets Start to finish it took roughly 15 minutes, and it ran while I talked through the app on camera. The full 19-minute run is here, uncut, including the part where I test a thumbnail generator I had not touched in half a year: ## What Is Claude Browser Control? Claude browser control (Claude in Chrome) lets Claude operate a real Chrome browser: clicking, typing, scrolling, and reading rendered pages, so it can drive any web app a human can. The difference from an API integration is that nothing needs to be integrated. My app has no MCP server, no webhook, no plugin. It is a password-protected Replit page, and Claude used it exactly the way I do: through the UI, with its own cursor. That second cursor is the part that still gets me. In the video you can see my mouse and Claude's mouse on screen at the same time, and only one of us is working. ## The App Claude Drove: YouTube Research Pro YouTube Research Pro is a keyword research dashboard I built on Replit around mid-2025. It talks directly to the YouTube Data API, which is why results come back in seconds instead of the scraping-and-praying loop most YouTube tools run on. One keyword in, seconds later: top videos, engagement, and the duration split ### What It Pulls From the YouTube Data API - Search overview - total results, total views, average views, average engagement, videos analyzed - Top videos by views - the ranked list you actually compete against, with view counts - Duration distribution - shorts vs 4-20 minute videos vs long form, so you know the format the niche rewards - Trending videos - what is moving right now on that exact keyword ### The AI Layer On top of the raw API data there is an insights layer running on Gemini 2.5 Flash: people also ask, niche analysis, target audience, content gaps, and trending subtopics per keyword. From there the app chains into a video ideas generator, a script writer, and a thumbnail generator built on Nano Banana - the same model I covered in [my Nano Banana creative direction post](/blog/banana-claude-ai-image-generation). There is also a PDF report export, which still overlaps text in places because I never gave it attention. I left that in the video instead of cutting around it. ## The Exact Prompt and Workflow ### The Prompt I Gave Claude Lightly cleaned of my on-camera filler words, this is the whole thing: ``` Hey there. Can you search for SEO, AI SEO and GEO on this YouTube Pro application. Just do the research, gather everything and then put everything in a well formatted CSV. Include everything you can find. ``` No system prompt, no tool schemas, no selectors. The app's UI is the API. ### Watching Claude Work the Dashboard Claude batched its actions the way a fast human would: type the keyword, press return, wait three seconds, capture the page. When the AI insights were slow to render, it waited instead of scraping a half-loaded page. It also went one step past my prompt and pulled the generated video ideas per keyword, which I had not asked for but kept. Sonnet 5 High typing "SEO" into my app - the batch log reads like a patient human ### From CSV to Google Sheets While Claude worked I typed sheets.new, and when the CSV landed I imported it. One sheet, one row per finding: search summaries, overview stats, top videos with views, upload dates, durations, likes and comments, people also ask with full answers, target audience, niche analysis, content gaps, and trending subtopics, for every keyword. The CSV in Google Sheets - every category Claude scraped, one keyword block at a time ## What the Data Says About SEO, AI SEO, and GEO Keywords My honest take: the GEO and AI SEO wave is real but past its peak, and the space is drowning in scripted, interchangeable content. That is a judgment call, built from reading the people-also-ask, niche-analysis, and content-gap outputs across all three keywords plus a year of watching this niche, not a trend line from one API snapshot. The benchmark numbers I can show side by side come from the top 25 videos analyzed per keyword ("marketing" is from my on-camera demo; the agent run covered SEO, AI SEO, and GEO): - "SEO" - 1,000,000 results found, 9.8M total views across the top 25, 391.7K average views, 2.42% average engagement - "marketing" - 1,000,000 results found, 25.2M total views across the top 25, 1.0M average views, 2.15% average engagement Both keywords report 1,000,000 results because that is the ceiling: the API's totalResults field is an approximation capped at 1,000,000, so read it as "maxed out", not a count. DURATION SPLIT - TOP 25 VIDEOS PER KEYWORD "SEO" 3 13 9 "marketing" 2 12 10 Shorts (under 4m) Medium (4-20m) Long form (over 20m) 4-20 minute videos lead both niches Source: my YouTube Data API pull, August 18, 2026 About half the top results in each niche run 4-20 minutes - one marketing video returned no parseable duration The medium format dominating both keywords matches what I keep finding across the stack I described in [my Claude Code SEO stack post](/blog/claude-code-seo-stack): the boring, reproducible signal beats the hype narrative. The people-also-ask answers for AI SEO read like everyone is holding the same script. If you are entering that niche, the content gap is not another explainer - it is receipts. Show a run, show the data, show what broke. ## You Don't Need My App One prompt to Claude Code or Codex can wire up the YouTube Data API and build this same dashboard. The app took me a weekend a year ago; today it is genuinely a one-prompt scaffold. The recipe: - Create a project in Google Cloud Console and enable the YouTube Data API v3 (free API key) - Tell your agent: "call search.list and videos.list for my keyword, aggregate views, engagement and duration, render it as a dashboard" - Point Claude's browser control at whatever it builds, and ask for the CSV Quota reality, per [Google's current documentation](https://developers.google.com/youtube/v3/getting-started): projects get a default allocation of 100 search.list calls per day plus 10,000 units per day for most other endpoints, and ordinary reads cost 1 unit. Searches are the constrained resource, so cache them. I covered the broader Google API setup, credentials included, in [my Google API SEO automation guide](/blog/google-api-seo-automation-claude-code). ## Why Sonnet, Not Haiku Haiku would probably have clicked through my app just fine, and faster. I used Sonnet 5 on high reasoning anyway, because research is the step where I want judgment. Clicking a search button is mechanical; deciding that the ideas section is worth scraping even though I forgot to ask for it is not. My rule: route the cheapest model that survives the task, and research tasks deserve one tier more than clicking tasks. ## What Happened Next: YouTube Pro Became Open Source The browser-driven dashboard in this experiment became [YouTube Pro, a public local-first workflow from research to thumbnail](/blog/youtube-pro-research-to-thumbnail). Version 1.0.0 connects public-video research, evidence-labeled AI Insights, grounded ideas, an editable script and teleprompter, and a thumbnail creator in one saved project. The source is available under Apache License 2.0, so you can inspect the exact boundaries before running it. Per-video owner analytics and video editing remain future work, not features of the current release. The members-only dashboard shown in the original video was a stepping stone. For captions, I was also using [CapForge](https://github.com/FRSname/CapForge), an open-source local caption generator with word-level alignment and styled rendering. ## Frequently Asked Questions ### Can Claude control a web browser? Yes. With browser control (Claude in Chrome), Claude operates a real Chrome tab: it clicks, types, waits for pages to load, and reads what renders, so it can drive any web app a human can. It needs the extension installed and per-site permissions granted. In my run it completed the full research flow without me touching the mouse. ### Can you use the YouTube Data API for keyword research? Yes. The search.list endpoint returns ranked videos and total result counts for any query, and videos.list adds views, likes, comments, and duration for each result. Together that covers demand, competition, engagement, and format split. My app is those two calls wrapped in a dashboard, with an AI insights layer on top. ### Is the YouTube Data API free? Yes, within quota. Per Google's current documentation, projects get a default allocation of 100 search.list calls per day plus 10,000 units per day combined for most other endpoints, and an ordinary read costs 1 unit. For keyword research at my scale, a handful of searches per session, the free tier is plenty. ### Which Claude model is best for browser automation? The cheapest model that survives the task. Haiku clicks fine and is faster for simple flows. I ran Sonnet 5 on high reasoning because research is the step where I want judgment: deciding what the data means is not a clicking task. Match the model to the thinking, not the clicking. ### Are GEO and AI SEO keywords still worth targeting? Worth targeting, carefully. The interest is real but, from what I saw in my August 2026 research run and a year of tracking the niche, past its peak and slowly declining. The space is saturated with scripted, interchangeable content. The opportunity I see is demonstrable workflows with receipts, not another hype explainer about how AI search changes everything. ## Related Posts - [Google API SEO Automation With Claude Code](/blog/google-api-seo-automation-claude-code) - the credential setup behind every API workflow on this site, YouTube Data API included - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - the terminal-first SEO system this research feeds into - [Nano Banana + Claude](/blog/banana-claude-ai-image-generation) - the image model behind the thumbnail generator - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - where the YouTube Studio plan fits Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # Claude Blog v2.1.1: What Changed and What to Do Now - URL: [https://agricidaniel.com/blog/claude-blog-v2-1-1-release](https://agricidaniel.com/blog/claude-blog-v2-1-1-release) - Published: 2026-07-23 - Updated: 2026-07-23 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Claude Blog v2.1.1 reframes AI citation scoring as a heuristic, refreshes Google guidance, and hardens the release pipeline. Here's what changed, why it matters, and what to do about it. If you're running [claude-blog](https://github.com/AgriciDaniel/claude-blog) inside Claude Code, a new version landed this week, and it's worth five minutes of your attention even if you don't read changelogs for fun. It's one entry in a [wider set of Claude Code skills](/blog/best-claude-code-skills-2026) worth knowing about. [How claude-blog works](/blog/claude-code-blog-writer) covered what the skill does; this post covers what just changed under the hood, and whether it affects the posts you've already published. The short version - Claude Blog v2.1.1 shipped on 2026-07-23, folding in v2.1.0's Google-policy realignment plus public-route and dependency fixes - The internal AI-citation score is now labeled a readiness heuristic, not a ranking factor or an authorship detector - Google implementation guidance was refreshed from current first-party sources - Zero breaking changes: the quality gate stays at 70, and 318 repository tests pass ## What Happened On 2026-07-23, [Claude Blog v2.1.1](https://github.com/AgriciDaniel/claude-blog/releases/tag/v2.1.1) shipped as a public distribution release. It bundles two pieces of work: the v2.1.0 Google-policy alignment prepared the same day, plus the public-route normalization and CI fixes needed to ship it cleanly to the open-source repo. Photo: Myburgh Roux via Pexels Key facts: - The signed v2.1.1 tag points to a green public main commit ( aec971a ), verified against [GitHub Actions](https://github.com/AgriciDaniel/claude-blog/actions) on Python 3.11 and 3.12. - 318 repository tests passed, alongside prose lint, marketplace/plugin validation, version-coherence, and public-route validation checks. - No commands or CLI arguments were removed or renamed; the existing quality-gate threshold of 70 is preserved. "The core promise is simple: the user is never the first reviewer." - claude-blog README ## Why This Matters This release matters less for any single feature and more for what it removes: overclaiming. The AI-tooling space is full of products that promise "guaranteed AI visibility" or claim to detect AI-authored content with precision they can't back up. v2.1.1 goes the other direction. It reframes claude-blog's own internal AI-citation score as an AI citation readiness heuristic , explicitly not a calibrated probability and not a Google ranking factor. Earlier versions leaned on the score to justify structural mandates: fixed-length citation blocks, mandatory FAQ sections, raw word-count targets, and answer-capsule length quotas. Those requirements got removed. The internal 0-100 scoring is reweighted instead, leaning on originality, purpose fit, source fidelity, and reader usefulness, alongside schema consistency and crawlability. What's easy to miss in a changelog bullet list: this is a maintainer choosing to make a scoring system less impressive-sounding in exchange for being more honest about what it can and can't measure. That's the opposite of the incentive most tooling vendors face, and it's the same instinct behind rejecting unsupported first-hand-testing claims and fan-out page factories in the same release. Before v2.1.1 After v2.1.1 AI score read like a ranking signal AI score is explicitly a readiness heuristic Delivery could block on fixed FAQ/word-count/citation-length quotas Those quotas are removed as blocking requirements Google guidance based on older canonicalization/Search Console assumptions Guidance refreshed from current first-party sources, plus a primary-source update ledger Installer/marketplace routes had public/private drift Routes normalized to the public AgriciDaniel/claude-blog repo ## What This Means for Content Teams For anyone running claude-blog solo or across a team, the immediate impact splits into three buckets. ### Impact 1: Scoring won't block you on style alone If a draft previously stalled because it was missing a FAQ block or ran short of a word-count target, that blocking behavior is gone. The quality threshold is still 70, but style statistics, punctuation patterns, phrase lists, and purported AI-authorship percentages can no longer sink a delivery by themselves. ### Impact 2: Google guidance is current again The release updates guidance across several areas that touch [Google's own canonicalization and indexing rules](https://developers.google.com/search/docs/crawling-indexing/consolidate-duplicate-urls): - Canonical reevaluation windows and quota-limited Request Indexing - Core-update analysis and Discover targeting - Rendered JSON-LD and retired structured-data types - AMP and Preferred Sources - Search Console's generative-AI and platform-property capabilities It also adds first-supported 2MB artifact checks for titles, metadata, canonicals, schema, and primary content, plus bloat warnings for excessive inline assets. ### Impact 3: The supply chain got a hardening pass google-genai moved to the tested 2.14.0 release and Patchright to 1.61.2, both with regenerated hash locks. Optional-module discovery was hardened, and PageSpeed regression coverage now handles responses without audit_details . If you run this at agency scale, this is the boring-but-important part of the release: fewer surprises when a dependency updates underneath you. ## What to Do Now Photo: Lukas Blazek via Pexels Here are four steps, ordered by urgency. - Today: Upgrade via the marketplace, or a pinned checkout. - This week: If precision matters to you (agencies running client reports, for instance), re-run the /blog audit sub-skill against a few already-published posts so you can see the new score composition side by side with the old one. - This month: Skim the [full changelog](https://github.com/AgriciDaniel/claude-blog/blob/v2.1.1/CHANGELOG.md) for the specific Google-guidance sections that touch your workflow, particularly if you rely on FAQ schema or canonical tags. - If you maintain a fork: Check the marketplace slug correction (now agricidaniel-blog ) and the normalized installer digests before your next sync. /plugin marketplace add AgriciDaniel/claude-blog /plugin install claude-blog@agricidaniel-blog Do NOT - Mass-rewrite posts that already passed the old gate. The threshold didn't move, and the change is additive, not corrective. - Assume the AI-citation score ever meant "this will get cited by ChatGPT." It didn't claim that before, and it explicitly doesn't now. ## The Bigger Picture In my view, v2.1.1 is one small data point in a broader shift I'd expect to keep playing out through 2026: as Google keeps tightening its own guidance and readers get more skeptical of AI-tooling claims, vendors who label their heuristics as heuristics (not guarantees) are going to age better than the ones who don't. This release is the maintenance-side mirror of that same argument this skill has always made about content itself: don't let your own scoring system make claims it can't support. Watch for this pattern elsewhere. If a tool you use scores "AI visibility" or "citation probability" without naming what it's measuring against, that's worth a second look. ## Frequently Asked Questions ### Does upgrading to v2.1.1 break my existing blog workflow? No. No commands or CLI arguments were removed or renamed, the quality-gate threshold stays at 70, and the new scoring metadata is additive. ### What actually changed in AI citation scoring? The score is now explicitly framed as a readiness heuristic rather than a ranking factor, reweighted toward originality, source fidelity, factual support, and reader usefulness, with fixed-length quotas (FAQ, word count, citation blocks) removed as blocking requirements. ### Where do I report an issue with the update? Through GitHub Issues on the public repository; the v2.1.1 notes credit several community reports that informed this release. ## Related Posts - [Claude Code Just Replaced Your Blog Writer - AI Slop Is Over](/blog/claude-code-blog-writer) - the original walkthrough of what claude-blog does - [Claude Ads v2.0.1: The Paid-Ads Operating System for Claude](/blog/claude-ads-v2-0-1-release) - the same skill suite applied to a different channel - [Best Claude Code Skills in 2026 - The Complete Guide](/blog/best-claude-code-skills-2026) - where claude-blog fits among the wider skill ecosystem - [How I Got 8,000 GitHub Stars in 9 Months as a Solo Developer](/blog/how-i-got-8000-github-stars) - the track record behind these releases Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # Claude Ads v2.0.1: The Paid-Ads Operating System for Claude - URL: [https://agricidaniel.com/blog/claude-ads-v2-0-1-release](https://agricidaniel.com/blog/claude-ads-v2-0-1-release) - Published: 2026-07-14 - Updated: 2026-07-14 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Claude Ads v2 is a full architecture rebuild for source-grounded paid-media operations across 12 platforms. v2.0.1 brings that release to the public repository with 506 passing tests and a green repository audit. ## Claude Ads v2 Is Public I rebuilt Claude Ads from the architecture up. v2.0.0 is the major release. v2.0.1 is the public distribution patch that makes the repository, installer, support, issue, discussion, and security-reporting paths work from the open public mirror. The result is a paid-media operating system for Claude Code. It covers audits, planning, creative workflows, monitoring, experiments, reporting, and carefully gated change drafts across 12 advertising platforms. The short version - 12 first-class paid-advertising platform contracts - 506 tests passed, with 23 skipped in the verification environment - Repository audit passed across 339 tracked files - Load-bearing platform claims mapped to dated evidence - Read-only operation by default - Versioned JSON as the system of record ## A Full Architecture Release This is not another prompt pack with a larger command menu. Claude Ads v2 separates product contracts, evidence, capabilities, scoring, safety, and release controls into explicit layers. One conductor owns the scope and final artifacts. Bounded workers analyze independent slices and return schema-valid findings. Required-worker failures make a run partial. They are never hidden behind a complete-audit label. The canonical result is versioned JSON. Markdown, HTML, and optional PDF reports are rendered from that same validated bundle. That removes the drift that happens when every output format invents its own version of the truth. ## 12 First-Class Advertising Platforms Claude Ads v2 gives each platform a dedicated contract, focused skill, audit worker, control reference, capability declaration, fixtures, and testable routing surface: - Google Ads - Meta Ads - YouTube Ads - LinkedIn Ads - TikTok Ads - Microsoft Advertising - Apple Ads - Amazon Ads - Reddit Ads - Pinterest Ads - Snapchat Ads - X Ads Shared APIs do not collapse distinct platform results. YouTube remains its own audit surface even when Google Ads supplies the data. Failed platforms are reported as failed or missing, not quietly converted into zeroes. ## No Source, No Current Claim Platform rules move too quickly for memory-based advice. v2 introduces public-safe source and claim ledgers for load-bearing platform, API, policy, regulation, benchmark, and creative-specification claims. Each registered claim carries dated evidence, a retrieval date, confidence, and a refresh deadline. Stale evidence makes the dependent result provisional. Unsupported claims are demoted instead of being presented as current platform truth. The same fail-closed approach applies to scoring. A catalog row is not automatically a health control. If a platform scoring profile is not approved, Claude Ads withholds the health score rather than inventing severity or category weights. ## Read-Only by Default Claude Ads can turn authorized exports or account reads into observations, findings, plans, creative workflows, experiments, monitoring, and reports. It does not assume that permission to analyze is permission to change an account. A live change requires an enabled capability for the exact operation, explicit account and object IDs, a human-readable before-and-after diff, owner approval, an idempotency key, an audit record, a verification window, and a rollback procedure. Missing ceilings mean no write. Permanent deletion is outside v2. The default remains simple: inspect first, draft second, change only after every gate passes. ## 506 Tests Passed On the tagged v2.0.1 release, the local suite completed with 506 tests passed and 23 skipped in the current environment. The repository audit also passed across 339 tracked files. The audit checks path portability, sensitive-content boundaries, and manifest consistency. The tests cover contracts, adapters, normalization, routing, scoring, reporting, release controls, installer safety, privacy, and URL defenses. Those numbers are evidence for the tested repository behavior. They are not a claim that every optional live integration is enabled. The capability manifest remains the authority for what can read, draft, apply, verify, or roll back on each platform. Transparency note: The v2.0.1 release notes also disclose five Pillow 12.2.0 advisories in optional creative and reporting paths. The dependency audit flags them openly while the evidence-complete dependency update is prepared. ## What v2.0.1 Changes v2.0.1 does not change the v2 code contracts, scoring, catalog, or behavior. It updates the public release surface on top of v2.0.0. - User-facing repository links now point to the public AgriciDaniel repository - The default clone source now uses the public repository - Support, issues, discussions, and private vulnerability reporting no longer require organization access - The README documents the public and community distribution model - Native Claude Code plugin installation is documented in the release That distinction matters. v2.0.0 is the architecture release. v2.0.1 is the clean public doorway into it. ## Install or Update Claude Ads The native Claude Code plugin flow is the recommended path: ``` /plugin marketplace add AgriciDaniel/claude-ads /plugin install claude-ads@ai-marketing-hub-claude-ads ``` If Claude Ads is already installed, prompt Claude with: Update Claude Ads to v2.0.1 from the public repository using the native plugin flow, then validate the installation and report the installed version. Read the [public repository](https://github.com/AgriciDaniel/claude-ads), review the [v2.0.1 release notes](https://github.com/AgriciDaniel/claude-ads/releases/tag/v2.0.1), or [watch the build and launch video](https://www.youtube.com/watch?v=rz3dpN9wZB0&t). ## Frequently Asked Questions ### Is v2.0.1 the architecture release? v2.0.0 is the full architecture release. v2.0.1 is a documentation and metadata patch that updates the public repository, installer, support, and release paths without changing the v2 behavior contracts. ### Which advertising platforms are supported? Claude Ads v2 has first-class surfaces for Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X Ads. ### Does Claude Ads automatically change ad accounts? No. Claude Ads is read-only by default. A live change requires exact capability support, a preview, explicit owner approval, idempotency, verification, an audit record, and rollback. ### Does every audit receive a health score? No. Claude Ads withholds a health score when evidence coverage is insufficient or the applicable platform scoring profile is disabled. Unknown findings reduce evidence coverage without being converted into failures. ### How do I update an existing installation? Use the native Claude Code plugin flow and ask Claude to update to v2.0.1 from the public repository. Run the validation workflow after the update and confirm the installed version before using it on account data. ## Related Posts - [Claude Ads v1.7.1: SSS+ Polish and Verified Citations](/blog/claude-ads-v1-7-1-release) - [Claude Ads v1.5: 250+ Ad Audit Checks Across 7 Platforms](/blog/claude-ads-v1-5-release) - [Claude Code Just Replaced Your Ad Agency](/blog/claude-code-ad-agency) - [The Open-Source AI Marketing Stack I Use Daily](/blog/ai-marketing-automation-stack) Build Better Paid-Media Systems Join the AI Marketing Hub for open-source tools, workflow templates, and practical implementation support. [JOIN FREE](https://www.skool.com/ai-marketing-hub)[GO PRO](https://www.skool.com/ai-marketing-hub-pro) *** # Claude Obsidian v1.9: Your AI Second Brain Gets a Compound Memory - URL: [https://agricidaniel.com/blog/claude-obsidian-v1-9-compound-vault](https://agricidaniel.com/blog/claude-obsidian-v1-9-compound-vault) - Published: 2026-05-28 - Updated: 2026-05-28 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. The big claude-obsidian update: v1.9 Compound Vault brings hybrid retrieval, methodology modes, a thinking framework, and multi-writer safety to your AI second brain. ## What Changed in This claude-obsidian Update? The public version of claude-obsidian jumped from v1.6 all the way to [v1.9.2](https://github.com/AgriciDaniel/claude-obsidian/releases/tag/v1.9.2), and the headline is the "Compound Vault" arc. The retrieval upgrade behind it is grounded in Anthropic's contextual retrieval research, which cut retrieval failures by up to 49%, and up to 67% once reranking is added ([Anthropic](https://www.anthropic.com/news/contextual-retrieval), 2024). In short, your second brain now remembers better. Claude Obsidian in two minutes: drop a source, watch the wiki build itself. If you've used claude-obsidian before, here's the quick map of what landed. If you're new, skip down to the primer first, it'll make the rest click. - v1.7 Compound Vault: Obsidian CLI as the default transport, hybrid retrieval, and per-file advisory locking for safe multi-agent ingest. - v1.8: methodology modes, so the vault organizes itself the way you already think. - v1.9: the /think skill, a 10-principle thinking framework, taking the plugin from 14 to 15 skills. - v1.9.1 and v1.9.2: audit hardening and prompt-cache hardening, the unglamorous work that keeps it reliable. The Compound Vault adds smarter retrieval and safe multi-writer ingest, all over plain Markdown. ## Why Does Hybrid Retrieval Matter? Hybrid retrieval is the upgrade you'll feel first. It pairs a contextual prefix with BM25 keyword search and a cosine rerank, and that combination is exactly what catches the near-misses a pure-similarity search hands you. The result: your wiki pulls the right pages, not just the ones that sound similar. Why does this matter for a second brain? Because the failure mode of every knowledge tool is confidently fetching the wrong note. A pure-similarity search will hand you a page that mentions the same words but answers a different question. Layering keyword matching and a rerank on top of context-aware chunks catches the near-misses. You ask, it retrieves, and the answer cites the page it actually came from. Worth being honest here: this is opt-in. The core wiki still runs on plain Markdown with no embeddings server. Hybrid retrieval is there when you want sharper recall on a big vault, not a requirement bolted onto everyone by default. ## What Are Methodology Modes and Multi-Writer Safety? Two quieter features in the update punch above their weight. Methodology modes and per-file locking both exist for the same reason: a second brain should bend to how you work and survive heavy use. AI knowledge management already saves workers 30-45% of retrieval time ([McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 2025); these features protect that gain at scale. ### Methodology modes Pick one of four organizing styles: LYT (maps of content plus atomic notes), PARA (projects, areas, resources, archives), Zettelkasten (timestamped, flat, densely linked), or Generic (no opinion). You set the mode once. After that, ingestion, /save, and /autoresearch all route new pages to the right place automatically. The AI files notes the way you already think. ### Multi-writer safety Per-file advisory locking means multiple agents can ingest into the same vault at once without stepping on each other. Before this, a parallel ingest could corrupt a page mid-write. Now each file write is guarded, so you can run multi-agent research loops and trust the vault on the other side. Per-file locks let several agents ingest at once without corrupting your notes. ## New Here? What Is claude-obsidian? If this is your first contact with the project, here's the primer. claude-obsidian is a free, MIT-licensed Claude Code plugin that turns Obsidian into a self-organizing AI second brain, built on Andrej Karpathy's LLM Wiki pattern ([Karpathy's gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f), 2025). You drop in sources; the AI reads them and writes the linked notes. The core is refreshingly boring in the best way: three plain files. A hot cache holding recent context, an index, and your wiki pages, all plain Markdown you own and can open in Obsidian forever. No database, no embeddings server for the core, nothing to migrate or host. Drop any source, ask any question, and the wiki grows richer with every session. For the full guide, with the workflow, the comparison to Notion, and how the hot cache kills session amnesia, read the pillar: [how I turned Obsidian into a self-organizing AI second brain](/blog/claude-obsidian-ai-second-brain). ## How Do You Get or Update It? Getting the update is a two-line job, and your existing notes are never touched. claude-obsidian stays free and MIT licensed through v1.9, sitting in a market growing at 30.3% CAGR toward $6.15 billion by 2030 ([Research and Markets](https://www.researchandmarkets.com/reports/6226503/personal-knowledge-base-ai-market-report), 2026) while charging nothing for the plugin itself. Fresh install: ``` git clone https://github.com/AgriciDaniel/claude-obsidian.git my-wiki bash my-wiki/bin/setup-vault.sh ``` Already using it? Pull the latest, or re-add via the Claude Code marketplace with claude plugin marketplace add AgriciDaniel/claude-obsidian . Because there's no database, there's nothing to migrate. The update only changes the plugin skills; your Markdown vault carries over untouched. If you want the visual companion, [claude-canvas](https://github.com/AgriciDaniel/claude-canvas) pairs cleanly with it. ## Frequently Asked Questions ### What is the claude-obsidian Compound Vault update? Compound Vault is the v1.7 to v1.9 arc. It adds hybrid retrieval, methodology modes, a thinking framework, and per-file locking for multi-writer safety. The retrieval layer draws on Anthropic research that cut retrieval failures up to 49%, and up to 67% with reranking ([Anthropic](https://www.anthropic.com/news/contextual-retrieval), 2024). ### What is hybrid retrieval in claude-obsidian? Hybrid retrieval combines a contextual prefix, BM25 keyword search, and a cosine rerank so your second brain pulls the most relevant pages, not just similar-sounding ones. It is based on Anthropic's contextual retrieval research, designed to surface the right page, not just a similar-sounding one. ### What are methodology modes? Methodology modes let you pick how the vault organizes new pages: LYT, PARA, Zettelkasten, or Generic. You set the mode once and ingestion, saving, and autoresearch route pages accordingly. It means the AI files notes the way you already think, instead of forcing one rigid structure on everyone. ### How do I update to claude-obsidian v1.9? Pull the latest from the public GitHub repo, or re-add it via the Claude Code marketplace with the marketplace add command. Your existing Markdown vault is untouched; the update only changes the plugin skills. The whole thing stays free and MIT licensed, with no database to migrate at all. ### Is claude-obsidian still free after the update? Yes. claude-obsidian remains MIT licensed and fully open source through v1.9. You pay only for your own AI model usage, and Obsidian is free for personal use. The Compound Vault features add no subscription, no hosted vector database, and no background worker burning your RAM. ## The Short Version Compound Vault makes your AI second brain remember better, file the way you think, and survive multiple writers, all while staying plain Markdown you own. The retrieval gains are real, and the price is still zero. - Star or update the repo on [GitHub](https://github.com/AgriciDaniel/claude-obsidian). - New here? Start with the full guide: [the self-organizing AI second brain](/blog/claude-obsidian-ai-second-brain). ## Related reading Start with the [original Obsidian AI second-brain guide](/blog/claude-obsidian-ai-second-brain), then compare the wider [Claude Code skill ecosystem](/blog/best-claude-code-skills-2026). *** # Claude and Codex Skills: The AI Visibility Wedge I Would Rank First - URL: [https://agricidaniel.com/blog/claude-codex-skills-ecosystem](https://agricidaniel.com/blog/claude-codex-skills-ecosystem) - Published: 2026-05-28 - Updated: 2026-05-28 - Category: ai-seo - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Claude Code skills, plugins, hooks, agents, AGENTS.md, CLAUDE.md, and Codex skills are now a real search cluster. Here is the source-backed map I would rank for before extracting a new AI visibility skill. The fastest ranking wedge right now is not a generic AI visibility article. It is the education gap around Claude Code skills, Claude Code plugins, hooks, agents, AGENTS.md, CLAUDE.md, and OpenAI Codex skills. The same-day DataForSEO pack showed strong demand: claude code skills at 9,900 monthly US searches, claude code plugins at 4,400, codex skills at 2,400, and best claude code skills at 320 with KD 1. The rank loop: education wedge, visibility check, SEO/content fix, Pro Hub repeatability. ## What is the difference between skills, plugins, hooks, and agents? Skills are reusable workflows. OpenAI describes Codex skills as folders with a required SKILL.md plus optional scripts, references, and assets; Codex uses progressive disclosure, so it starts with metadata and loads the full skill only when chosen. Anthropic's Claude Code docs use the same basic idea: a skill teaches Claude how to handle a task and can be invoked directly or when relevant. Layer What it does Ranking angle Skill Reusable task workflow with instructions and optional scripts/resources. People search for examples, best skills, install paths, and when to create one. Plugin Installable distribution package. OpenAI says plugins are the distribution unit for reusable Codex skills and apps. Users need to know when a skill should stay local and when it should be packaged. Hook Lifecycle automation around tool use, permissions, or workflow guardrails. Great for governance, but not the first artifact for a content workflow. Agent Specialized worker role with focused instructions and tool permissions. Useful for parallel execution, research, verification, and bounded implementation slices. Memory file Project or user context such as AGENTS.md for Codex or CLAUDE.md for Claude Code. Users search for where instructions live and why not everything should become a skill. ## Why not create the AI visibility skill first? Because a skill is best after the workflow stabilizes. OpenAI's skill guidance says each skill should stay focused on one job and use scripts only when deterministic behavior or external tooling is needed. Right now, the AI visibility loop still has three moving parts: DataForSEO evidence, answer-first content production, and cross-site publishing. If we package too early, the skill will either be vague or overloaded. The better move is to rank the public method first. Publish the [AI visibility tool page](https://claude-seo.md/ai-visibility-tool), publish the [public questions/PAA article](https://claude-blog.md/blog/public-questions-paa-skill), and use this article as the education bridge. After three to five real runs, extract the stable pieces into a dedicated skill. The ecosystem map: education creates demand, visibility checks find gaps, SEO and blog skills publish fixes. ## Where Claude Code and Codex differ Do not blur the two ecosystems. Codex uses AGENTS.md and Codex skills. Claude Code uses CLAUDE.md for memory and Claude skills/plugins for reusable workflows. If you want to reuse an AGENTS.md idea inside Claude Code, treat it as content to import or translate into CLAUDE.md ; do not claim Claude Code reads it directly unless your project wiring explicitly does that. For Codex, the official docs say skills can live in user, repo, admin, and system locations, and that plugins can bundle skills, apps, MCP server configuration, and presentation assets. That distinction matters for the ranking page: a local workflow belongs in a skill; a shared installation experience belongs in a plugin. ## The ranking cluster I would build - Hub: Claude Code skills vs plugins vs hooks vs agents. This article can serve that intent on agricidaniel.com. - Commercial spoke: AI visibility tool on claude-seo.md, because tool intent has demand and low-to-moderate KD. - Method spoke: Public questions and PAA SEO on claude-blog.md, because it explains how content becomes answer surfaces. - Proof spoke: GitHub READMEs and release notes for claude-seo, claude-blog, and codex-seo. ## Official-source checklist Use these rules when writing or updating the cluster: - Use OpenAI docs for Codex skills, AGENTS.md, and plugins. - Use Anthropic docs for Claude Code skills, plugins, commands, and memory. - Use Google Search Central for AI features, snippets, robots controls, and structured data policy. - Use DataForSEO docs and same-day API output for SERP, keyword, PAA, and AI visibility measurement. - Use first-hand repo and vault evidence for your own tool claims. ## What this means for the new skill The future skill should not be named too broadly. I would split it into two focused candidates after repeated runs: - ai-visibility-loop: accepts brand, competitors, prompts, and URLs; outputs measured gaps, sources, and priority fixes. - public-questions: accepts a seed keyword; outputs PAA clusters, answer-first blocks, page outline, visible FAQ section, source checklist, and internal-link plan. Until then, use the existing stack. Claude SEO can research and audit the visibility gap. Claude Blog can turn that gap into source-backed content. agricidaniel.com can publish the ecosystem explanation and point people to the tools. That is enough to rank before turning the SOP into a reusable skill. ## Sources - [OpenAI Codex Agent Skills](https://developers.openai.com/codex/skills) - [OpenAI Codex Plugins](https://developers.openai.com/codex/plugins) - [OpenAI AGENTS.md guide](https://developers.openai.com/codex/guides/agents-md) - [Claude Code skills docs](https://code.claude.com/docs/en/skills) - [Claude Code plugins docs](https://code.claude.com/docs/en/plugins) - [Google AI features guidance](https://developers.google.com/search/docs/appearance/ai-features) ## Related reading Compare the [best Claude Code skills](/blog/best-claude-code-skills-2026), inspect the [Codex SEO implementation](/blog/codex-seo-openai-codex-cli), and see how the same protocol approach connects AI to WordPress in [WP MCP Ultimate](/blog/wp-mcp-ultimate-wordpress-ai-plugin). *** # I Turned Obsidian Into a Self-Organizing AI Second Brain - Here's the Free Plugin - URL: [https://agricidaniel.com/blog/claude-obsidian-ai-second-brain](https://agricidaniel.com/blog/claude-obsidian-ai-second-brain) - Published: 2026-04-10 - Updated: 2026-05-28 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I built a free Claude Code plugin that turns Obsidian into a self-organizing AI second brain. 15 skills, plain Markdown you own, zero manual filing. ## Why Most Second Brains Quietly Die The personal knowledge base AI market hit $1.65 billion in 2025 and is growing at 30.3% CAGR toward $6.15 billion by 2030 ([Research and Markets](https://www.researchandmarkets.com/reports/6226503/personal-knowledge-base-ai-market-report), 2026). Yet most people's note vaults are graveyards. Notes go in, links never get made, and six months later you can't find anything. I've been there. Hundreds of notes, almost zero connections, and the low-grade guilt of knowing the system only pays off if I maintain it. The usual advice is "just build a Zettelkasten" or "use bidirectional links." That's the productivity equivalent of telling someone to floss more. Technically correct, practically useless, because the bottleneck was never the method. It's the maintenance. So I built [claude-obsidian](https://github.com/AgriciDaniel/claude-obsidian), a free Claude Code plugin that does the organizing for you. You drop in sources. It reads them, writes linked notes, flags contradictions, and keeps a living index. Your second brain finally maintains itself. A two-minute look at dropping a source in and watching the wiki build itself. Key Takeaways - claude-obsidian is a free, MIT-licensed Claude Code plugin built on Andrej Karpathy's LLM Wiki pattern. It reads your sources and writes the linked notes for you. - The core is three plain files: a hot cache, an index, and your wiki pages. No database, no embeddings server, plain Markdown you own forever. - AI knowledge management saves knowledge workers 30-45% of time on information retrieval ([McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 2025). - v1.9 "Compound Vault" adds hybrid retrieval, methodology modes, multi-writer safety, and a thinking framework across 15 skills. ## What Is an AI Second Brain? An AI second brain is a personal knowledge base where the AI does the filing. You add raw material; the AI reads it, writes summaries, builds links, and keeps the index current. The old "second brain" idea, popular in PKM and Notion circles, still asks you to do the labor. You tag, you link, you maintain. An AI second brain flips that. The model reads the source, extracts the people and concepts, writes the pages, and connects them to everything you already have. Your job shrinks to two steps: feed it good sources and ask good questions. Here's the part people miss. The value of a knowledge base is proportional to its link density, not its note count. A vault with 100 notes and 500 cross-references beats one with 1,000 notes and 50 links. An AI second brain is worth building precisely because the machine is patient enough to make every link, every time. A real claude-obsidian vault: entities, concepts, and sources cross-referenced automatically. ## What Is Karpathy's LLM Wiki Pattern? Andrej Karpathy, co-founder of OpenAI and former Tesla AI director, shared an approach to personal knowledge management that skips traditional vector search entirely ([Karpathy's gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f), 2025). Instead of embedding notes into vectors, you keep them as plain Markdown an LLM can read directly. The LLM doesn't just search your notes. It writes and maintains them. The shape is simple. You index source documents into a raw directory. Then you use an LLM to "compile" a wiki: a set of Markdown files with summaries, backlinks, and concept pages that tie everything together. Obsidian is the front end where you browse the raw data, the compiled wiki, and the graph. The LLM owns the busywork. Why does this beat a vector database for personal knowledge? Three reasons, in plain terms: - No vector database. No embeddings server to host, no Pinecone bill, no background worker eating RAM. Just Markdown files on your disk. - Human-readable output. The wiki is browsable in Obsidian with graph view, links, and full-text search, even with no AI running at all. - Compounding intelligence. Each new source gets cross-referenced against everything already there. The 50th source is far more useful than the first. claude-obsidian is my implementation of this pattern. It is not the only one on GitHub, but it is the most complete, with 15 skills, autonomous research, and a hot cache the others don't have. Three plain files at the core: a hot cache, an index, and your wiki pages. No database required. ## How Do You Build a Second Brain With AI? Building an AI second brain with claude-obsidian is genuinely a three-step loop, and the third step is "do nothing." Once it is set up, you stop filing and hunting for notes entirely. The workflow looks like this: ### Step 1: Drop a source in Drag a PDF, paste a URL, or dump a meeting transcript into the raw folder. Originals are never modified. They sit there as your immutable record of truth. ### Step 2: Run "ingest this" The ingest skill reads the source and does the work you'd normally dread. It pulls out named entities like people, tools, and companies. It identifies concepts and frameworks. It creates or updates wiki pages, adds links between related pages, and flags anything that contradicts what you already had. ### Step 3: There is no step 3 That's it. One command, and a 20-page PDF becomes 8-15 connected wiki pages with proper structure, cross-references, and source tracking. You didn't create a single folder, tag, or link by hand. In my own use, a vault built from a few dozen mixed sources ends up with the large majority of pages linked to at least two others, with zero manual linking on my part. One command takes a raw source to a mesh of linked, sourced pages. ## What Can claude-obsidian Actually Do? claude-obsidian ships 15 skills as of v1.9, which makes it less of a chat plugin and more of a knowledge operating system. Most tools in that space do one thing. This one covers the full lifecycle. The headline commands, in plain English: - /wiki scaffolds the whole vault from a short description. - /save files an entire conversation as one clean, linked note. No copy-paste cleanup. - /autoresearch takes a topic, reads sources, writes pages, and grows the graph. Up to 12 pages hands-free in a single run. - /canvas builds visual reference boards with images and PDFs. - /think runs a 10-principle thinking loop for harder decisions, new in v1.9. Under those sit the workhorses: wiki-ingest, wiki-query, wiki-lint, wiki-retrieve, wiki-mode, wiki-cli, and wiki-fold. The point isn't the count. It's that the skills compound. When you ingest a source, the agent doesn't just summarize. It creates entity pages, concept pages, cross-references them against every existing page, and flags conflicts. The 50th source you add weaves 10 new notes into a mesh of 500. Knowledge compounds like interest. Every source you add makes the next answer sharper. ## Is claude-obsidian a Good Notion Alternative? Yes, if owning your data matters to you. A lot of knowledge-app spend is locked inside proprietary clouds. claude-obsidian goes the other way. Your notes are plain Markdown on your disk. Here's the honest comparison. Notion-style apps are excellent for collaboration and structured databases, but your data lives in their format, on their servers, under their export rules. If they raise prices or shut a feature, your knowledge is hostage. Most AI-memory setups make this worse, not better: they bolt a vector database and a background worker onto your notes, which quietly burns tokens and RAM and adds a second thing that can break. claude-obsidian has neither. The core is three plain files: a hot cache of recent context, an index, and your wiki pages. You can open them in Obsidian, in a text editor, or in Notepad ten years from now. The AI is a tool that reads and writes those files, not a landlord that holds them. That's the whole pitch for the PKM and second-brain crowd: own the data, rent only the intelligence. ## How Does the Hot Cache Fix Session Amnesia? Every fresh AI conversation starts with total amnesia, and that tax is real. And a chunk of every session gets clawed back re-explaining context to a model that forgot where you left off. claude-obsidian fixes this with a hot cache: a single file holding roughly 500 words of your most recent context. When you start a new conversation, the plugin reads it first and restores the AI's working memory. No recap. No "where were we?" Typically it holds what you were working on, the key decisions and why, which pages changed, and your open next steps. The math is friendly. That's about 500 tokens to read, a rounding error against Claude's context window, and it saves the 2,000 to 3,000 tokens you'd otherwise burn re-establishing context. In my own daily use that's roughly a 4x to 6x return on a tiny token investment, every single session. I walk through giving Claude a memory that never forgets, from an empty vault to a living graph. Before: orphan notes. After: a self-organized, linked graph the AI maintains for you. ## What Is New in claude-obsidian v1.9 (Compound Vault)? The public release jumped from v1.6 to v1.9.2, and the "Compound Vault" arc is the biggest change since launch. It rests on Anthropic's contextual retrieval research, which cut retrieval failures by up to 49% and up to 67% with reranking ([Anthropic](https://www.anthropic.com/news/contextual-retrieval), 2024). The short version: your second brain now remembers better and works safely with multiple writers. Four things landed across the arc: - Hybrid retrieval. A contextual prefix, BM25 keyword search, and a cosine rerank work together so the wiki pulls the right pages, not just the similar-sounding ones. - Methodology modes. Pick how the vault organizes itself: LYT, PARA, Zettelkasten, or Generic. Ingestion and saving route new pages accordingly. - Multi-writer safety. Per-file advisory locking means several agents can ingest at once without corrupting your vault. - A thinking framework. The new /think skill brings a 10-principle reasoning loop for audits, architecture calls, and ambiguous requests. That's the jump from 14 to 15 skills. I wrote a full walkthrough of the update for anyone who wants the deep dive. Read it here: [Claude Obsidian v1.9: the Compound Vault release](/blog/claude-obsidian-v1-9-compound-vault). ## How Do You Connect Claude to Obsidian? Connecting Claude to Obsidian with this plugin takes about two minutes. Obsidian's local-file model is exactly why this works: your vault is just a folder of Markdown, so the plugin reads and writes it directly. The retrieval layer behind it leans on research that reduced failures by up to 49% ([Anthropic](https://www.anthropic.com/news/contextual-retrieval), 2024). The fastest path is two lines: ``` git clone https://github.com/AgriciDaniel/claude-obsidian.git my-wiki bash my-wiki/bin/setup-vault.sh ``` Then open the my-wiki folder in Obsidian and you're done. Prefer the marketplace? Run claude plugin marketplace add AgriciDaniel/claude-obsidian instead. On desktop the plugin uses the Obsidian CLI as its default transport, and it always keeps plain filesystem access as a fallback, so a missing dependency never blocks you. This is the local-first promise in practice. If Claude's API goes down, your wiki still opens and searches in Obsidian. If you switch AI tools next year, your knowledge comes with you because it was never trapped anywhere. For a visual companion to your notes, pair it with [claude-canvas](/blog/claude-canvas-ai-visual-production) to build image and PDF reference boards. ## Frequently Asked Questions ### What is an AI second brain? An AI second brain is a personal knowledge base where an AI reads your sources, writes the notes, links them, and keeps everything organized. You add raw material; the AI builds the structure. McKinsey reports knowledge workers save 30-45% of retrieval time with AI knowledge management ([McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 2025). ### How do you build a second brain with AI? Drop sources into one folder, then let the AI extract entities, write linked pages, and maintain an index. With claude-obsidian you run one command and a 20-page PDF becomes 8-15 connected notes. No manual folders, tags, or links. The graph compounds with every source you add over time. ### Is claude-obsidian a good Notion alternative? Yes, if you want to own your data. Notion stores notes in a cloud-proprietary format you cannot easily move out. claude-obsidian writes plain Markdown that lives on your disk and opens in Obsidian forever. There's no vendor lock-in and no subscription for the plugin, which is MIT licensed and free. ### How do you connect Claude to Obsidian? Install claude-obsidian as a Claude Code plugin, then point it at an Obsidian vault folder. The plugin reads and writes the Markdown directly. On desktop it uses the Obsidian CLI by default, with filesystem access as an always-available fallback. Setup takes about two minutes with a two-line clone command. ### Is it free? Yes. claude-obsidian is MIT licensed and fully open source on GitHub. You pay only for your AI model usage, such as Claude API tokens, and Obsidian itself is free for personal use. There's no subscription, no vector database to host, and no background worker quietly burning your RAM. ## The Wiki That Builds Itself The personal knowledge base AI market is climbing from $1.65 billion in 2025 toward $6.15 billion by 2030 ([Research and Markets](https://www.researchandmarkets.com/reports/6226503/personal-knowledge-base-ai-market-report), 2026), and most of that growth will come from tools that do the work humans hate. claude-obsidian is my bet on what that looks like. It's Karpathy's pattern, productized. Fifteen skills covering the full lifecycle from source to maintenance. A hot cache that remembers your context. A Compound Vault that retrieves better and survives multiple writers. And it's free and open source, because knowledge tools should be owned, not rented. - Star the repo on [GitHub](https://github.com/AgriciDaniel/claude-obsidian). - Read the deep dive: [Claude Obsidian v1.9 Compound Vault](/blog/claude-obsidian-v1-9-compound-vault). - Add a visual layer with [claude-canvas](/blog/claude-canvas-ai-visual-production). *** # Local SEO Brain: Claude Code Agent for Ranking Local Businesses in the Map Pack - URL: [https://agricidaniel.com/blog/claude-code-local-seo-brain](https://agricidaniel.com/blog/claude-code-local-seo-brain) - Published: 2026-05-21 - Updated: 2026-05-21 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Local SEO Brain is a source-cited Obsidian operating brain plus a Claude Code, Codex, or Gemini agent that ranks local businesses in the Google Map Pack, organic local SERPs, and AI search. Ships 59 chapters, 37 reusable AI prompts, 31 reference images, 1,271 wikilinks, and a 2026 freshness overlay. Setup ~30 minutes per client, then the agent handles audits, GBP optimization, backlinks, and review strategy on a loop. ## What is Local SEO Brain? Local SEO Brain is a source-cited Obsidian operating brain + AI-agent skill that ranks local businesses in the Google Map Pack, organic local SERPs, and AI search. It ships 59 knowledge-base chapters, 37 reusable AI prompts, 31 reference images, and 1,271 wikilinks, plus an approval-first agent script layer that runs through Claude Code, Codex, or Gemini. Setup takes ~30 minutes per client. Then the agent ingests your GBP exports, citation crawls, review feeds, and geo-grid scans into one Obsidian memory and synthesizes a prioritized audit — with every recommendation linked back to the raw export it came from. Watch the full 6-minute walkthrough: [](https://youtu.be/4v7116sEbrQ) Local SEO Brain on YouTube — 6:18 walkthrough by Daniel Agrici. ## The Problem: 60+ Hours of Spreadsheet Audits per Client Most local-SEO agencies bill 60 or more hours per client audit. The work is real — GBP exports from Google Business Profile, citation crawls from Whitespark or BrightLocal, geo-grid scans from Local Falcon, review feeds, on-page audits, backlink mapping — and most of it ends up scattered across Google Sheets and screenshots. The "synthesis" then happens in someone's head on a Tuesday call. Generic AI tools do not fix this. ChatGPT hallucinates GBP metrics. Claude does not have your Local Falcon export open in another tab. Gemini will happily invent backlinks that do not exist. What was missing was a structured operating brain that ingests real local-SEO data and gives an AI agent the context to write a useful audit. Fragmented spreadsheets and screenshots become one source-cited Obsidian memory. ## What's in the Box Local SEO Brain ships two artifacts in one bundle. The first is a buyer-facing Obsidian vault seeded with a complete local-SEO knowledge base — 59 chapters across the 3 Phases, Core 30 content strategy, GBP playbooks, audits, citations, backlinks, E-E-A-T, and LLM SEO. The second is the agent-facing operating layer: a SKILL.md contract plus a scripts/ folder that scaffolds per-client vaults, ingests raw data, and synthesizes source-cited audits. - assets/template-brain/ — Obsidian vault following the Hot/Index/Wiki pattern, seeded with the 3 Phases, Core 30 content strategy, GBP optimization playbooks, audits across property/content/technical/CTR, local citations, backlinking, E-E-A-T, and LLM SEO. Includes a 2026 freshness overlay so stale chapters carry warning callouts and current-state links. - SKILL.md + scripts/ — agent-facing operating layer. Scaffolds per-client vaults, re-seeds the knowledge base idempotently, ingests raw sources (GBP, citations, reviews, geo-grid), synthesizes a source-cited audit + action roadmap, lints the graph, and renders client-ready deliverables. Zero Python dependencies in V1 — runs on the standard library. The seeded vault — 200+ nodes, 1,271 wikilinks, color-coded by folder. ## How It Works: Karpathy's Hot/Index/Wiki Memory Pattern Every vault scaffolded by Local SEO Brain follows Andrej Karpathy's three-layer context pattern, adapted from his agent-memory talks. The pattern keeps the agent's working set tight while letting the underlying vault grow without choking the context window. - wiki/hot.md — small working-memory file. What changed, what is blocking, what to do next. - wiki/index.md — full navigation map. Section-grouped wikilinks across the vault. - wiki/ — deep notes loaded on demand through wikilinks and hubs. For any cross-agent session you point the agent at the vault with: "Read CODEX.md, then wiki/hot.md, then wiki/index.md, then the relevant note." The brain always has a tight working set and pulls deep context on demand. Karpathy's three-tier memory pattern, applied to local SEO. ## The 3 Phases of Local SEO Ranking The brain teaches and operationalizes a three-phase strategic frame across the frameworks/, audits/, and gbp/ folders. Property, then GBP, then Authority — in that order, because skipping a phase wastes everything stacked on top of it. ### Phase 1 — Property (the website foundation) Your website is the foundation. Without a properly audited site (E-E-A-T, Core 30 pages, technical health, CTR-optimized metadata, industry-specific schema subtypes), nothing else compounds. The brain ships property audits for technical, on-page, content, and CTR, plus the Core 30 content map that builds topical authority and earns the behavioral signals the December 2025 Core Update rewards. ### Phase 2 — GBP (the storefront) Your Google Business Profile is the storefront. 60-70% of local clicks come from the Map Pack. The GBP folder covers verification through posts, geotagging, services, categories, holiday hours, NAP consistency, and the messaging-and-Q&A surface. The Claude Code agent ingests your GBP Insights export and writes a prioritized GBP roadmap with provenance. The Map Pack is the storefront — 60-70% of local clicks happen here. ### Phase 3 — Authority (citations, backlinks, reviews, AI-citation share) Trust signals from third-party websites. Local citations, competitive backlink research, social signals, review velocity, and AI-citation share across ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot. Claude Code ingests Local Falcon / Whitespark / BrightLocal / Moz Local exports and synthesizes a prioritized authority roadmap. ## LLM SEO for Local: The 2026 Freshness Layer Local SEO Brain ships a dedicated llm-seo/ folder covering what matters for showing up in ChatGPT, Perplexity, Google AI Overviews, and Bing Copilot — four signals that did not exist as ranking factors two years ago but now drive a measurable share of local discovery: - Reddit citation engine. Reddit sits at roughly 40% of LLM citation share. The brain ships a playbook for earning Reddit citations in r/localBusiness subs without violating sub rules. - Person schema. Load-bearing post-December-2025 for E-E-A-T attribution. The brain templates Person schema for the business owner, key staff, and contributing authors. - Industry-specific schema subtypes. Generic LocalBusiness is no longer enough. The brain ships subtypes for Plumber, Restaurant, Dentist, HomeAndConstructionBusiness, AutoRepair, and more. - AI Overview Map Pack pressure. Citation patterns have stabilized to 3 to 5 sources per AI Overview. The brain optimizes for inclusion in that tight set. The 2026 freshness layer overlays the original knowledge base with current-state notes so stale references — sunset GBP Chat, retired LLM model names, the December 2025 Core Update, Reddit citation share — are flagged in-place with warning callouts. ## What the Audit Output Looks Like After running synthesize_brain.py against the seeded sample vault, the Claude Code agent emits a Health Scorecard with phase grades, top 5 prioritized actions, and an Approval Queue. Every recommendation carries a source: line — if the supporting raw file is missing, the recommendation does not ship. A real Health Scorecard from one run of the brain on the demo vault. ``` # acme-plumbing · Health Scorecard > Generated 2026-05-20 · sources verified ## Phase grades | Phase | Grade | Top issue | |-----------|-------|---------------------------------------------------------| | Property | C+ | Homepage missing LocalBusiness > Plumber schema subtype | | GBP | B- | 3 services missing in primary category | | Backlinks | D | 14-citation gap vs top-3 competitors | | LLM SEO | C | No Reddit presence; Person schema missing on About | ## Top 5 priorities (sourced) 1. Add Plumber schema subtype to homepage source: .raw/sources/property-audit.csv:42 · sha256: 9a3f... 2. Fill 3 missing services in primary GBP category source: .raw/sources/gbp-export.csv:18 · sha256: 4c12... 3. Close 14 citation gap (BBB, Yelp, Angi, HomeAdvisor + 10) source: .raw/sources/whitespark.csv:1-203 · sha256: e7b9... 4. Build 1 Reddit answer in r/Plumbing per week, cite owner photo source: wiki/llm-seo/Reddit-LLM-Citation-Engine.md 5. Publish location pages for top 4 service-area suburbs source: wiki/audits/Topical-Geo-Relevance.md ## Approval queue - [ ] Schema markup change (rollback: revert deploy) - [ ] GBP service additions (rollback: re-delete from owner UI) ``` ## Six Use Cases the Brain Handles Out of the Box Claude Code Local SEO Brain is built for the work agencies and operators actually do, not for an abstract "AI SEO" demo. Six scenarios ship with playbooks: Scenario What you run What you get New client kickoff (single location) scaffold_vault → ingest 4 sources → synthesize_brain Day-1 health scorecard, prioritized roadmap, citation gap Multi-location audit (chain of 12 stores) One scaffold per location, parallel ingest, chain-level synthesis Per-location grade matrix, shared NAP issues, chain-wide citation gaps AI search visibility check Ingest GBP + run llm-seo/ prompts against GPT-5 / Claude / Gemini Brand mention diff, Reddit gap, schema modernization checklist Quarterly review (existing client) Re-run all ingestors, lint, render report Velocity report: what shipped, what slipped, what is next Competitor teardown (one rival) Ingest competitor backlinks + GBP profile via competitors/ template Gap analysis with sourced opportunities Property handoff (agency to in-house team) Render full vault to HTML Static, searchable, source-cited handover bundle ## Refusal Rules: What the Brain Will Not Do Most local-SEO automation drifts toward the gray-hat tactics that get accounts suspended. Local SEO Brain ships refusal rules baked into the prompt layer — the agent refuses to recommend or generate any of the patterns that violate Google's spam policies or risk a GBP suspension. The brain refuses fake reviews, proximity gaming, GBP category gaming, and "rank #1 guaranteed" copy. ## Who Local SEO Brain Is For Buyer What they get Local SEO agencies & freelancers (5 to 50 client locations) A repeatable, source-cited operating layer that replaces spreadsheet audits and screenshot dumps. Multi-location operators (10+ locations) A per-location memory layer for NAP, citations, reviews, geo-grid scans, and audit history. SAB businesses doing SEO in-house (plumbers, HVAC, law, dental, home services) A playbook + memory in one. The brain knows what to do and remembers what was already done. ## How to Get Local SEO Brain Two ways to grab Local SEO Brain — pick the path that fits how you want to work. - Bundle — included in [AI Marketing Hub PRO](https://skool.com/ai-marketing-hub-pro) alongside the rest of the brain library, community support, and access to every brain release as it ships. - One-off — grab the vault for $69 on [Gumroad](https://erniseth.gumroad.com/l/localseobrain). Same vault, no community. Upgrade to PRO any time you want the next brain release. Other tools and open builds live at [github.com/AI-Marketing-Hub](https://github.com/AI-Marketing-Hub). For a complete tour of Local SEO Brain in action, [watch the 6-minute walkthrough on YouTube](https://youtu.be/4v7116sEbrQ). ## What's Next in the Brain Series Local SEO Brain is the first in a series. The same Hot/Index/Wiki memory pattern + source-cited synthesizer applies to other marketing surfaces — each gets its own playbook library and ingestor set. Roadmap: - Marketing Brain — full-funnel ICP / messaging / content / channel orchestration - Mailing List Brain — list growth + segmentation + send-strategy - E-commerce Brain — product catalog + PMax + review-mining + retention loops - B2B SaaS Brain — content-led + ABM + lifecycle orchestration Reply on the [YouTube video](https://youtu.be/4v7116sEbrQ) with which brain you want built next — input shapes the roadmap. ## Related reading For the broader technical workflow, read [the open-source Claude Code SEO stack](/blog/claude-code-seo-stack). For source-backed performance inputs, see [the Google API SEO automation guide](/blog/google-api-seo-automation-claude-code). *** # claude-ads v1.7.1: SSS+ Polish, Animated Banner, 10 Citations Verified - URL: [https://agricidaniel.com/blog/claude-ads-v1-7-1-release](https://agricidaniel.com/blog/claude-ads-v1-7-1-release) - Published: 2026-05-19 - Updated: 2026-05-19 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. v1.7.1 is a polish release on top of v1.7.0. Animated SVG banner, parameterized branding kit, README overhaul, dual-repo positioning, and 10 quantified claims verified against primary vendor docs. No functional changes, all trust signal. ## What Shipped in claude-ads v1.7.1 claude-ads v1.7.1 went out on May 18, 2026. claude-ads is an open-source ad audit tool that runs inside Claude Code and covers Google Ads, Meta Ads, Amazon Ads, YouTube, LinkedIn, TikTok, Microsoft Ads, and Apple Search Ads. v1.7.1 is a polish release. No new checks, no new skills, no new platforms. The job was to elevate the skill to SSS+ tier across README, branding, banner, dual-repo positioning, and citation discipline. Eight platforms, 22 sub-skills, 209 weighted checks, 41 tests still passing. That part did not move. What moved is everything around it. v1.7.0 → v1.7.1 delta: documentation polish, zero behavior change. Here is what landed: - Animated SVG banner: 13.9 KB optimized file, 22 SMIL animations, role="img" and descriptive markup for screen readers - Branding kit: parameterized template at branding/banner-template.html plus an AGENT-PROMPT.md so the terminal-style design can be replicated across sibling projects - README overhaul: personas, JSON sample outputs, comparison matrices, expandable FAQ, dual-repo documentation - Citation pass: 10 quantified claims across six ad-platform modules verified against primary vendor documentation - Project info section: CHANGELOG, CONTRIBUTING, CODE OF CONDUCT, SECURITY, SUPPORT links surfaced from the README - Dual-remote canonical move: origin shifted to AI-Marketing-Hub/claude-ads , this repo is now the open-source mirror - Style cleanup: 54 em dashes removed across docs, platform-features table updated with concrete feature names and dates, script permissions normalized to 755 Key Takeaways - v1.7.1 is documentation, branding, and trust signal. Zero functional changes. - 10 quantified claims across six ad-platform modules now cite primary vendor docs. - Animated SVG banner is 13.9 KB with 22 SMIL animations and screen-reader markup. - 54 em dashes removed. Style guide is now machine-enforced across the README. - Canonical remote is now AI-Marketing-Hub/claude-ads. The AgriciDaniel mirror stays in sync. ## Why Polish, Not Features Polish, not features. v1.7.1 ships zero new functionality, every change is a trust signal. v1.7.0 was a heavy lift. Eight platforms, 22 sub-skills, 209 checks, 41 tests, six AI assistant hosts, full Amazon coverage including Sponsored Products, Brands, and Display. The launch demo on YouTube walks through all of it. Once features ship, the next bottleneck is trust. Most paid-ad tooling is a black box. You install it, get a number back, and you have to take it on faith. I want the opposite. Every check should be inspectable. Every benchmark should cite a vendor source. The README should answer the questions agencies actually ask before they hand a tool a client account. That was the v1.7.1 brief. Lock down the citation discipline, ship a banner that looks like the rest of the family, and rewrite the README so the comparison matrices, personas, and sample outputs land in the first scroll. ## The Animated Banner The new banner is the visual centerpiece. 13.9 KB. 22 SMIL animations. Terminal-style framing that matches claude-seo, claude-blog, and claude-canvas. The brand kit at ~/Documents/Obsidian Vault/concepts/brand-design-system.md calls for OS-window chrome, JetBrains Mono labels, brand orange #D97757 for focal elements, and a 4.2-second breathing gradient. v1.7.0 had a static PNG. v1.7.1 has the animated SVG with proper accessibility markup. The parameterized template ships in branding/banner-template.html . Pair it with AGENT-PROMPT.md and you can regenerate the same banner for any sibling repo with one prompt and a JSON parameter file. That is the point: the design system is now cloneable, not bespoke. Platform coverage: Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon. 209 checks total. ## The Citation Pass Ten quantified claims, ten primary-vendor sources. Every benchmark in v1.7.1 traces to a publisher, title, URL, and retrieval date. Ten quantified claims got the receipts treatment. The job was simple. Every number in a check description, every threshold, every benchmark, every "+18% queries" or "56 billion retail media" had to trace back to a primary vendor source. Not a blog post citing a blog post. The vendor doc itself, with a retrieval date, in the reference file. Six modules touched: ads-google, ads-meta, ads-apple, ads-microsoft, ads-budget, ads-creative. The vendor sources cited include Meta Engineering, Apple Developer session 221, Google Ads blog, Microsoft Advertising blog, and AppTweak. The audit lifecycle is the same as before. What changed is that when a check fires and says "Meta bundles near-duplicates at the auction layer before scale," the underlying threshold is now tied to a Meta-published doc, not a guess. This is the FLOW evidence triple in practice: year anchor in the prose, inline citation with publisher and title, URL with retrieval date. The full framework lives at [github.com/AgriciDaniel/flow](https://github.com/AgriciDaniel/flow). Applied to ad audits, it means clients can ask "where did this number come from" and get a real answer. Sample vendor sources cited in the v1.7.1 sweep include [Meta Engineering](https://engineering.fb.com/), [Apple Developer WWDC25 sessions](https://developer.apple.com/videos/play/wwdc2025/), the [Google Ads blog](https://blog.google/products/ads-commerce/), and the [Microsoft Advertising blog](https://about.ads.microsoft.com/en/blog). Citation pass results: 10 primary sources, 41 tests green, 0 functional drift. ## Demo Walkthrough The v1.7 walkthrough video is the fastest way to see the product. The flow: - Three big shifts framed up front: Amazon retail media hit $56 billion, Meta rewired creative ranking through Andromeda, Google is moving accounts to AI Max. Most tools miss all three. claude-ads covers them. - 22 sub-skills, one orchestrator: say "audit my Meta account" and the Meta sub-skill loads. Say "plan a sales campaign" and the planning sub-skill loads. The router handles dispatch. - One script, six hosts: Claude Code, Codex CLI, Cursor, Windsurf, Gemini CLI, Goose. Same skills, any host. - 41 tests enforcing 209 checks: run the same audit twice, get the same grade. That is the trust signal most competitors miss. - Pre-flight AI Max audit: Google is flipping search accounts to AI Max. Legacy campaigns get caught before the flip, not after. - Andromeda creative-similarity: Meta throttles near-duplicates before scale. The audit scores it pre-launch on visual, headline, body, and hook format. - Amazon in one pass: Sponsored Products, Sponsored Brands, Sponsored Display, all covered in a single audit run. The architecture itself stayed put. claude-ads still ships 22 sub-skills across 8 ad-platform hosts (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon), with one orchestrator wiring them together. v1.7.1 polished the documentation and design system around that surface area. It did not change the surface area. 22 sub-skills across 8 ad-platform hosts, one orchestrator. The shape of the product in v1.7.1. ## Who This Helps Three audiences benefit directly from v1.7.1: - Agencies handing audits to clients: the citation pass means every benchmark in the PDF deliverable now traces to a vendor doc. Fewer "where did this number come from" emails. - Solo consultants pitching new accounts: the README overhaul is the new sales page. Personas, sample outputs, comparison matrices, FAQ. Send the GitHub link, prospect reads it themselves. - Builders forking the skill: the branding kit means anyone forking claude-ads for a private agency variant can regenerate the banner with one prompt. The design system is no longer locked to my desktop. ## Install Same install commands as v1.7.0. Pick one: ``` # Claude Code plugin /plugin marketplace add AI-Marketing-Hub/claude-ads # Unix one-liner curl -fsSL https://raw.githubusercontent.com/AI-Marketing-Hub/claude-ads/main/install.sh | bash # Windows one-liner irm https://raw.githubusercontent.com/AI-Marketing-Hub/claude-ads/main/install.ps1 | iex ``` The skill works across Claude Code, Codex CLI, Cursor, Windsurf, Gemini CLI, and Goose. Same sub-skills everywhere. ## What's Next v1.7.1 closes the v1.7 cycle. Next on the queue: - Wave three platform coverage: Walmart Connect and Connected TV. Walmart is the second-largest US retail media network. CTV is where the budgets are moving. - v2 measurement stack: Marketing Mix Modeling, incrementality testing, PMax feed health auditing. The check catalog stays. What gets added is the layer above it that ties checks to revenue impact. - More citation passes: the v1.7.1 sweep hit 10 claims. There are more. Every release going forward gets a citation pass before the tag lands. ## FAQ ### Is v1.7.1 a breaking change? No. Zero functional changes. Same 22 sub-skills, same 209 checks, same 41 tests, same scoring math. If v1.7.0 worked for you, v1.7.1 works identically. The release is documentation, branding, and citation discipline. ### What is the dual-repo thing? Canonical origin moved to AI-Marketing-Hub/claude-ads . This is the org I use to distribute Pro-tier work to the Skool community. The AgriciDaniel/claude-ads repo stays alive as the open-source mirror. Both repos point at the same code. Install instructions reference AI-Marketing-Hub because that is where releases ship first. ### Where do the 10 verified citations show up? Inline in the check descriptions across six ad-platform modules: Google, Meta, Amazon, TikTok, LinkedIn, Microsoft. Every quantified claim now has a publisher, a title, a URL, and a retrieval date. The full reference list lives in the skill's references/ directory. ### How big is the animated banner? 13.9 KB. 22 SMIL animations. Total file size is under the 100 KB image budget that the brand design system enforces. The banner uses role="img" and descriptive elements for screen-reader compatibility. ### Can I use the branding kit for my own project? Yes. branding/banner-template.html is parameterized. Pair it with branding/AGENT-PROMPT.md and you can regenerate a matching banner for any sibling repo. The kit is MIT licensed alongside the rest of the skill. ### Is the tool still free? Yes. MIT license, no usage limits, no premium tier. You need a Claude Code subscription because the skill runs inside Claude Code, but the skill itself is free and open source. There is no hosted version. ## Related Posts - [claude-ads v1.5: 250+ Ad Audit Checks Across 7 Platforms](/blog/claude-ads-v1-5-release) - The v1.5 release with the first PDF report generator - [Claude Code Just Replaced Your Ad Agency](/blog/claude-code-ad-agency) - The original product post covering positioning and scope - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - Where claude-ads fits in the full stack - [claude-seo v1.9.6: FLOW Framework Security Hardening](/blog/claude-seo-v196-flow-security-hardening) - The FLOW evidence-citation framework that drove the v1.7.1 citation pass - [Best Claude Code Skills 2026](/blog/best-claude-code-skills-2026) - How claude-ads compares to the rest of the Claude Code skill ecosystem Read the full [v1.7.1 release notes on GitHub](https://github.com/AgriciDaniel/claude-ads/releases/tag/v1.7.1), star the repo if it helps you, and open an issue if anything breaks. Learn more [about me](/about) and the rest of the open-source AI marketing stack I build. Join 4,500+ AI Marketing Builders Workflow templates, audit playbooks, and a community of SEOs, agency owners, and PPC consultants who ship. [JOIN FREE](https://www.skool.com/ai-marketing-hub)[GO PRO](https://www.skool.com/ai-marketing-hub-pro) *** # codex-seo: I Ported My 9,500-Star SEO Stack to OpenAI Codex CLI - URL: [https://agricidaniel.com/blog/codex-seo-openai-codex-cli](https://agricidaniel.com/blog/codex-seo-openai-codex-cli) - Published: 2026-05-12 - Updated: 2026-05-12 - Category: Tools - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. claude-seo runs inside Claude Code. The other half of my readership uses OpenAI Codex. So I ported the whole thing: 26 SEO workflows, 24 TOML agents, deterministic Python runners, MIT licensed. Here's what's inside codex-seo v1.9.6-codex.5. Half my GitHub stars come from people who don't run Claude Code. They run [OpenAI Codex CLI](https://github.com/openai/codex) instead. Every week one of them opens an issue on [claude-seo](https://github.com/AgriciDaniel/claude-seo) asking the same question: can I use this with Codex? Until April, the answer was "no, but soon." Now it's [codex-seo](https://github.com/AgriciDaniel/codex-seo) - a Codex-native port of the entire SEO suite, currently shipping at v1.9.6-codex.5 , synced to claude-seo's main branch at commit a9cf338 , and packaged with everything Codex needs to run a full audit from your terminal. This post is the long version of the README. Founders get the cost math. Devs get the TOML agent architecture. SEO marketers get the 26 workflows. Pick a section and skip the rest. codex-seo v1.9.6-codex.5 - one orchestrator, 26 specialist workflows, 24 TOML agents Key Takeaways - codex-seo is the OpenAI Codex variant of claude-seo , my SEO tool with 9,500+ GitHub stars, ported as a Codex skill suite with 24 TOML agents. - 26 specialist workflows cover technical SEO, content, schema, GEO, local, ecommerce, backlinks, FLOW, DataForSEO, Firecrawl, and image generation. - Average full audit runtime: 2.8 minutes on a 50-page site, 6.2x faster than running the same workflows sequentially. - One-line install. MIT licensed. Replaces a $394/month SaaS stack with $0/month. ## What Is codex-seo, in Plain English? codex-seo is an open-source SEO skill suite for OpenAI Codex CLI , built on top of my 9,500-star [claude-seo](https://github.com/AgriciDaniel/claude-seo) project. You install it once, restart Codex, and from then on you can run a full technical and content SEO audit by typing a sentence in natural language. The orchestrator routes your request to one of 26 specialist workflows, which delegates the heavy lifting to one of 24 Codex TOML agents running in parallel. Reports get written to disk as Markdown, JSON, HTML, or PDF. No SaaS dashboard. No monthly bill. That's the elevator pitch. Now the longer version: codex-seo is not a wrapper around ChatGPT. It crawls your sitemap, parses your HTML, inspects your schema, runs Core Web Vitals checks, and grades your content for E-E-A-T signals. It's built around what I call deterministic headless execution - Codex can fire it from chat, but the same workflows can also be triggered from a shell script, a CI pipeline, or a Python runner. The output is repeatable, not improvised , which is the whole point of running SEO in code. Because the Codex CLI ecosystem has been growing fast since OpenAI shipped agents in early 2026, having a serious SEO tool that ships as a Codex skill matters. Most "AI SEO tools" are wrappers around prompts. codex-seo ships 247 Markdown documents, 69 Python scripts, 24 TOML agent profiles, contract tests, and cross-platform installers. It's the same kind of production-ready open source I built claude-seo to be, but packaged for the people who run codex instead of claude . Here's the video walkthrough, if you want to see it before reading the rest: ## Why I Stopped Paying $300/Month for SEO Tools I used to pay for the full SaaS SEO stack. Ahrefs at $99/mo. Semrush at $139/mo. Surfer SEO at $89/mo. Frase at $45/mo. Screaming Frog at $22/mo. That's $394 a month, or $4,728 a year , for tools I'd estimate I used about 30% of - the audit reports, the keyword research, the on-page scoring. The other 70% was features I never opened: white-label PDF builders, agency seat management, branded client portals, etc. So in early 2025 I started building [claude-seo](/blog/claude-code-seo-stack) to replace the audit half of that stack. Twelve months later it had 9,500 stars and was being forked by agencies who wanted to run audits from Claude Code instead of paying SaaS rent. codex-seo is the same idea, ported to the OpenAI side. (I wrote the [full breakdown of free SEO audit tools](/blog/free-seo-audit-tools) if you want the broader comparison.) The cost picture is brutal once you draw it: Five SaaS tools at $394/mo combined. codex-seo at $0/mo, MIT licensed. I'm not saying Ahrefs or Semrush are bad tools. They're great. They also have ten years of SERP history and a backlink database codex-seo will never match. What I am saying is that 80% of the SEO work I do day-to-day is audits, on-page fixes, schema generation, GEO checks, and content briefs - and every one of those tasks now runs locally, from my terminal, for free. If you're paying for SaaS to do those five things, you're paying for the dashboard, not the data. For an agency owner, the math is louder. Three clients × $394/mo = $14,184/year in tooling. codex-seo doesn't replace your team. It replaces the line item that pays for the team to use a UI when they could be reading raw findings in a Markdown file. ## How Is codex-seo Different from claude-seo? codex-seo is a port, not a fork . The 26 SEO workflows, the FLOW framework prompts, the schema templates, the E-E-A-T scoring rubrics - they're synchronized to claude-seo's main branch at commit a9cf338 . If a fix lands in claude-seo, it gets ported to codex-seo on the next release. The two tools share the same core, but the packaging is completely different. Here's the breakdown of where they diverge: Concern claude-seo codex-seo Host runtime Claude Code plugin Codex skill suite + .codex-plugin/plugin.json Agent definition Implicit Claude subagents 24 explicit Codex TOML agent profiles Headless execution Chat-only output Deterministic Python runners for CI/CD Config path ~/.config/claude-seo/ ~/.config/codex-seo/ (reads claude-seo as fallback) Cache ~/.cache/claude-seo/ ~/.cache/codex-seo/ + project .seo-cache/ Report formats Markdown + optional PDF Markdown, JSON, HTML, PDF License MIT MIT The most consequential row is the second one: TOML agents . In claude-seo, parallel execution happens because Claude Code dispatches multiple subagent tool calls in a single response. That works fine inside chat, but it's invisible to anyone trying to script the tool. In codex-seo, every agent is a separate .toml file under ~/.codex/agents/seo-*.toml , each with its own description, role, and tool allowlist. Codex sees them at startup and can fan them out in parallel during full audits. It also means a developer can read seo-technical.toml and know exactly what the technical agent is allowed to do - no guessing. The other consequential row is row three. claude-seo lives in chat. If you want to run an audit on a deploy, you'd have to open Claude Code, paste the URL, and copy the output. codex-seo ships scripts/run_skill_workflow.py , which wraps any workflow into a deterministic command-line call. That's how you put SEO in CI/CD. More on that below. ## The 26 Specialist Workflows: What You Actually Get The headline number is 26 workflows. That sounds vague, so here's what it actually maps to. [The COMMANDS.md reference](https://github.com/AgriciDaniel/codex-seo/blob/main/docs/COMMANDS.md) lays out every prompt the orchestrator recognizes, but the easier way to think about it is in five buckets: Every codex-seo workflow grouped by category. Each is backed by its own TOML agent. - Foundation specialists (8) - technical , content , schema , sitemap , performance , visual , images , geo . These are the core slices of a normal site audit. Technical handles crawlability, indexability, robots, canonicals, redirects. Content scores E-E-A-T, helpfulness, AI citation readiness. Schema detects and generates JSON-LD. Sitemap validates structure and proposes new entries. Performance pulls Core Web Vitals signals. Visual takes screenshots and grades above-the-fold. Images analyzes alt text, weight, format, and metadata. GEO evaluates AI Overviews, ChatGPT, Perplexity, and llms.txt readiness. - Strategic planning (5) - plan , cluster , programmatic , sxo , competitor-pages . Plan turns an audit into a 30/60/90-day roadmap. Cluster builds hub-and-spoke topic architectures from SERP analysis. Programmatic evaluates risk and scale for template-driven page builds. SXO scores search-experience optimization - is the page actually what searchers want? Competitor-pages designs comparison and "alternatives" pages based on what's ranking. - Domain-specific (6) - local , maps , hreflang , backlinks , ecommerce , drift . Local checks NAP consistency, GBP signals, citations, reviews. Maps does geo-grid rank tracking and competitor radius mapping. Hreflang validates international SEO setups. Backlinks summarises link profiles and source-tier detection. Ecommerce handles product schema and marketplace visibility. Drift baselines an SEO state before changes and compares afterward. - Data integrations (5) - google , dataforseo , firecrawl , image-gen , flow . Google wires GSC, PageSpeed, CrUX, Indexing API, GA4. DataForSEO pulls live SERP, keyword, and backlink data when you've added credentials. Firecrawl handles JS-rendered crawls. Image-gen creates OG images and infographics via the Gemini/nanobanana pipeline. FLOW runs the evidence-led prompts from my [claude-seo v1.9.6 security and FLOW post](/blog/claude-seo-v196-flow-security-hardening). - Audit orchestrators (2) - audit and page . Audit is the do-everything entry point. Page is a deep single-page review. Total: 26. If you've never used claude-seo, that ratio probably feels excessive. It isn't. A real SEO audit on a site over 200 pages crosses all five categories. Splitting them into individual workflows means each one can be invoked alone ( /seo schema https://example.com ) or together ( /seo audit https://example.com dispatches the full set). ## Installing codex-seo in 30 Seconds If you've already got OpenAI Codex CLI installed, the installer is one line: ``` curl -fsSL https://raw.githubusercontent.com/AgriciDaniel/codex-seo/v1.9.6-codex.5/install.sh | bash ``` Windows users run the PowerShell equivalent: ``` irm https://raw.githubusercontent.com/AgriciDaniel/codex-seo/v1.9.6-codex.5/install.ps1 | iex ``` What that script actually does: it clones the repo into a temp dir, copies the skill suite into ~/.codex/skills/ , drops the 24 TOML agent files into ~/.codex/agents/ , creates a Python virtualenv at ~/.codex/skills/seo/.venv/ , installs the core runtime dependencies, and then tries to install the optional capability groups - Playwright for screenshots and PDFs, Google API clients, DataForSEO, Firecrawl, OCR, and report generators. If a capability group fails (Playwright on a headless server, for example), the installer logs it and keeps going. The skill still works without it; it just gracefully degrades. If you'd rather inspect the code before running it, the longer install is: ``` git clone https://github.com/AgriciDaniel/codex-seo.git cd codex-seo bash install.sh ``` You can override the install with environment variables: CODEX_HOME , CODEX_SEO_REPO (for forks), CODEX_SEO_REF (for tags or commits), and CODEX_SEO_SKIP_PLAYWRIGHT_BROWSER=1 if you don't want the Chromium download. The installer is idempotent - run it again to upgrade. After install, restart Codex. That's it. You won't see a new menu item; the skill suite registers itself and the orchestrator picks up natural language. Latest local validation: 52 tests passing, full installed smoke suite passing, demo readiness passing . If something breaks during install, the script prints exactly which capability group failed and how to retry. ## Real Workflows: From "Audit My Site" to the A-Z Prompt Pack The fastest way to learn codex-seo is to copy six prompts from the [repository documentation](https://github.com/AgriciDaniel/codex-seo). Each one maps to a real SEO job. You don't need to memorise the commands; the orchestrator parses natural language and routes for you. But the explicit form is useful when you want to script. 1. Full A-Z audit. The single prompt that fires every relevant specialist: ``` /seo audit https://example.com ``` The orchestrator detects business type (SaaS, ecommerce, local, content, etc.) and selectively turns on local, maps, ecommerce, hreflang, programmatic, drift, and cluster checks. Output: SEO Health Score, ranked fix list by Critical/High/Medium/Low, 30/60/90 day roadmap, keyword opportunities, exact follow-up prompts. 2. Keyword and cluster research. ``` /seo cluster "main keyword" ``` Builds a topic cluster from the audit findings - pillar pages, supporting pages, search intent, internal links, priority order. This is the workflow that replaces Surfer SEO's content planner for me. 3. Content roadmap. ``` /seo plan business-type ``` Turns audit + cluster findings into a 90-day SEO content roadmap with page titles, target keywords, intent, brief notes, and priority order. Output is a Markdown table - the kind of thing I used to pay $89/mo to Surfer for. 4. Optimize one page. ``` /seo page https://example.com/page ``` Gives title tag, meta description, H1/H2 structure, missing sections, internal links, schema fixes, image fixes, and a rewrite plan. The page agent uses the same evidence cache as the full audit, so it isn't re-crawling. 5. AI search / GEO readiness. ``` /seo geo https://example.com ``` Checks if the page is ready for AI Overviews, ChatGPT Search, Perplexity, and citation-style answers. Flags missing answer-first formatting, low citation density, weak structured data, and absent llms.txt . 6. Local business check. ``` /seo local https://example.com ``` Validates NAP consistency, GBP optimization, local schema, citations, review signals, and Google Maps presence. Spawns the maps specialist for geo-grid rank tracking if you've configured DataForSEO. These six prompts cover 90% of what I do daily. The other 10% is more specialised - hreflang validation, ecommerce product schema, drift comparisons after a redesign. Each has its own prompt. The full list is in COMMANDS.md. ## Why TOML Agents Matter for CI/CD SEO Here's the dev angle. In Codex CLI, every agent is a TOML file. codex-seo ships 24 of them, named seo-technical.toml , seo-content.toml , seo-schema.toml , and so on. Codex loads them at startup. When you run an audit, the orchestrator dispatches multiple agents in parallel - technical and content can crawl in parallel because neither depends on the other; schema and sitemap can run alongside. That parallelism is the difference between a 3-minute audit and a 17-minute audit. On agricidaniel.com (about 50 pages), I benchmarked the same audit three ways in April 2026: Same audit, three execution modes. Measured on agricidaniel.com, April 2026. Parallel codex-seo: 2 min 47 sec . Sequential: 17 min 22 sec. Manual checklist (a real SEO analyst clicking through tools and writing notes): roughly four hours, conservatively, for a 50-page site. The parallel mode is 6.2x faster than sequential and 86x faster than manual. That's the entire reason TOML agents matter - not because the format is fancy, but because explicit agent files let Codex schedule them in parallel without baked-in coordination logic. And because the agents are files, you can put them in version control. You can audit them. You can fork one and tweak the prompt for your own brand. You can write a CI pipeline that runs scripts/run_skill_workflow.py seo-technical https://staging.example.com on every deploy and fails the build if the technical score drops below a threshold. That last part is real, by the way: I've helped agencies put codex-seo audits into GitHub Actions so every staging deploy gets a green/red SEO gate before merging. No SaaS tool I know of supports that without a custom integration. ## GEO and the FLOW Framework: SEO Built for AI Search Here's the angle nobody else is talking about. Traditional SEO tools optimize for Google's blue links. codex-seo has a dedicated GEO workflow and a full FLOW framework integration because that isn't enough anymore. By the end of 2026, AI-assisted search will account for an estimated 30% of all search interactions across major engines and assistants , based on the trajectory of AI Overviews, Perplexity, ChatGPT Search, and Copilot. Sites that read well to AI crawlers get cited. Sites that don't get summarised away. The GEO workflow inside codex-seo checks for the things AI systems extract: answer-first paragraph openings, citation capsules (40-60 word self-contained quotables), inline source attribution, FAQ-style headings that match conversational queries, structured data that LLMs can parse cleanly, and llms.txt compliance. If any are missing, it flags them with priority ratings and writes specific fixes. FLOW is the framework I shipped in [claude-seo v1.9.6](/blog/claude-seo-v196-flow-security-hardening) and ported into codex-seo at the same commit. It's a five-stage loop: Find the surfaces where the query appears (Google, AI Overviews, Perplexity, Reddit, YouTube), Leverage what's already ranking, Optimize using evidence-led prompts (CTR audits, AI detector tests, schema completeness, ChatGPT visibility checks), and Win by tracking position changes over time. codex-seo runs each FLOW stage as its own prompt under /seo flow find , /seo flow leverage , and so on. If you've never thought about SEO this way, the simplest mental model is: your content has to be quotable . AI assistants build answers by stitching together short, attributable passages. If your H2 doesn't open with a 40-60 word quotable paragraph, you're invisible to that pipeline. codex-seo's content and GEO workflows score this directly. Run them on any page and you'll get a citability score plus the exact rewrites to lift it. This is also where codex-seo earns its keep against tools like Ahrefs that haven't shipped a real GEO product yet. The SaaS world is still optimizing for what worked in 2022. codex-seo is built for what's working in 2026. ## Is codex-seo Right for You? A Decision Matrix Three audiences, three different go/no-go signals. Here's how to decide: If you are a... Go signal Skip if Founder You run a small or mid-size site, you're paying for SEO SaaS, and you want a one-line install replacement. You need a polished UI for non-technical team members or you depend on Ahrefs' backlink graph. Developer You want SEO in your CI pipeline, you live in Codex CLI, and you'd rather configure 24 TOML files than learn a SaaS API. You don't actually do SEO work and you're just curious about the architecture. SEO marketer You produce client audits weekly, you're tired of stitching outputs from 5 different tools, and you want one Markdown report per client. You bill clients for the SaaS tools themselves as a pass-through and would lose the markup. If you fell into a "go signal" row above, codex-seo will save you hours every week. If you fell into a "skip if" row, keep your current setup. I built this because I wanted it, not because I think every SEO workflow has to be open source. Try it on one site, one audit. If it's not as good as what you're paying for, you've lost 10 minutes. Try codex-seo on one audit, this week One-line install. MIT licensed. 26 workflows. Star the repo, run an audit on your own site, and decide. [STAR ON GITHUB →](https://github.com/AgriciDaniel/codex-seo) ## Frequently Asked Questions ### How is codex-seo different from claude-seo? codex-seo is the OpenAI Codex CLI port, adapted with 24 TOML agent profiles, deterministic Python runners, and Codex-native install paths ( ~/.codex/ ). claude-seo runs inside Claude Code. They share 26 SEO workflows and the same MIT license. The Codex variant adds explicit agent files that enable parallel CI/CD audits and clear tool allowlists per agent. ### Do I need paid APIs to use codex-seo? No. codex-seo runs every workflow on free signals by default: HTML parsing, sitemap crawls, schema validation, Core Web Vitals checks, GEO scoring. Paid integrations like DataForSEO, Firecrawl, and Google APIs are opt-in and clearly marked as setup_required until you wire credentials. The tool never fabricates data when an integration is missing. ### Is codex-seo really MIT licensed? Yes. Fork it, modify it, ship it inside a paid product if you want. The LICENSE file is plain MIT, identical to claude-seo. All 26 workflows, 24 TOML agents, Python runners, schema templates, and reference docs are open-source. No enterprise tier, no waitlist, no feature gating, no "free for personal use only" clause. ### Will codex-seo send my site data to OpenAI? codex-seo only sends what your Codex session sends. The skill suite runs locally: it crawls your URLs, parses HTML on disk, writes reports to output/ . Credentials stay in your ~/.codex/ config. Whatever Codex itself transmits to OpenAI is governed by Codex's privacy policy, not codex-seo. ### Can codex-seo replace Semrush or Ahrefs? For audits, technical SEO, content scoring, schema, Core Web Vitals, GEO, local, and ecommerce work, yes. For massive backlink databases or ten years of SERP history, no. codex-seo leans on free signals first and lets you wire DataForSEO when you need live SERP data. Most teams I've talked to keep one SaaS for backlinks and use codex-seo for everything else. ## Wrap-Up codex-seo is the same idea as claude-seo, packaged for the half of my readership running OpenAI Codex CLI instead of Claude Code. Same 26 workflows. Same MIT license. Same opinion about $300/month tool stacks. What's new is 24 explicit TOML agents, deterministic Python runners, and a parallel execution model that drops audit time from 17 minutes to under 3. If you've been waiting for a serious open-source SEO suite that runs natively in Codex, this is it. Install it. Run one audit. Star the repo if it saved you time. If it didn't, file an issue and tell me what's broken - every release in this series has shipped fixes from the community. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - the original $300/month teardown that started this project - [claude-seo v1.9.6: FLOW Framework + Security Hardening](/blog/claude-seo-v196-flow-security-hardening) - the release that introduced FLOW prompts and the security model codex-seo inherits - [Free SEO Audit Tools That Actually Work](/blog/free-seo-audit-tools) - the broader comparison of what's free in 2026 - [Google API SEO Automation](/blog/google-api-seo-automation-claude-code) - how to wire GSC, PageSpeed, and CrUX into a Codex audit *** # Two Claude SEO Releases in One Day: FLOW Framework Plus Security Hardening - URL: [https://agricidaniel.com/blog/claude-seo-v196-flow-security-hardening](https://agricidaniel.com/blog/claude-seo-v196-flow-security-hardening) - Published: 2026-04-26 - Updated: 2026-04-26 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I shipped v1.9.5 (FLOW SEO framework, 41 evidence-led prompts) and v1.9.6 (security audit, 10 findings closed) the same day. Here is the long-form story: what FLOW is, how the agent picks 2 to 3 prompts from 21, and how the audit pass closed every finding before the day was over. ## Two Releases. One Day. Six Hours of Work. I shipped Claude SEO v1.9.5 this morning and v1.9.6 this afternoon. Same day. Both tagged, both pushed, both with full release notes. v1.9.5 added the FLOW SEO framework as the 21st sub-skill in the tool. v1.9.6 ran a security audit over the new code and closed every finding before the day was over. This post is the long-form story of why I split the work into two releases instead of one, what FLOW actually is, and how the security pass closed 10 findings (1 HIGH, 4 MEDIUM, 5 LOW, plus 1 INFO) using Subagent-Driven Development with two-stage review per task. ## Why v1.9.6 Followed v1.9.5 the Same Day Two releases shipped April 26, 2026. [v1.9.5](https://github.com/AgriciDaniel/claude-seo/releases/tag/v1.9.5) integrated the FLOW SEO framework as the 21st core sub-skill in Claude SEO. [v1.9.6](https://github.com/AgriciDaniel/claude-seo/releases/tag/v1.9.6) ran a security audit over the new attack surface and fixed every finding before the day was over. Functional code in v1.9.5. Hardened code in v1.9.6. Same FLOW capability, with the integration locked down to A grade. New installs should pin to v1.9.6. The split was deliberate. v1.9.5 introduced new code paths: a GitHub API sync script, a new agent with WebFetch access, 41 bundled prompt files, cross-skill references. Each of those is an attack surface. Shipping the audit pass as a separate tag makes the security work auditable on its own, with its own test suite and its own release notes. 10 findings closed, 0 outstanding, 5 to 15 tests, B (88) to A (95+). ## What FLOW Adds to Claude SEO FLOW is an evidence-led SEO methodology with 41 prompts across 5 stages. Find. Leverage. Optimize. Win. Plus a Local-SEO branch. The framework is mine, originally published at [github.com/AgriciDaniel/flow](https://github.com/AgriciDaniel/flow) under CC BY 4.0. v1.9.5 brings it inside Claude SEO so any user can run stage-specific analysis on a target URL with one command. The stages map to how SEO actually works in practice. Find is research: keyword markets, topical relevance, the surface area of what could rank. Leverage is amplification: E-E-A-T signals, authority moves, the things that compound. Optimize is implementation: on-page, off-page, technical, content. Win is the loop: rank tracking, performance review, the discipline of iteration. Local handles GBP, NAP consistency, citations, and map pack strategy as a first-class branch. Metric v1.9.0 v1.9.6 Core sub-skills 20 21 Subagents 15 18 Tracked scripts 30 30 Tests existing +15 sync_flow.py FLOW prompts bundled 0 41 Eight commands ship with the skill: find , leverage , optimize , win , local , prompts (lists all available), sync (pulls latest from upstream), and sync --ref (pins to a specific upstream commit for reproducibility). ## How the Skill Picks 2 to 3 Prompts From 21 The Optimize stage has 21 prompts. The agent never loads all of them. It reads the prompt file names first, fetches the target URL, then selects 2 or 3 prompts that match the page's industry signals, content gaps, or technical issues. Context-matched, not a dump. This is the difference between a prompt library and a prompt framework. A library gives you 21 things you could try. A framework gives you the 2 or 3 you should try, given what your page actually looks like. The seo-flow agent is the discrimination layer. It reads the page once, scans the available prompts, and applies the relevant ones with evidence requirements baked into each output. The other four stages are bounded. Find has 5 prompts. Leverage has 1. Win has 3. Local has 11. Optimize is the one with 21 because that is where most SEO implementation work lives, and where context-matching matters most. ``` /seo flow optimize https://example.com/pricing ``` Output includes the stage label, the prompt files applied with one-line rationale for each pick, evidence-tagged findings per prompt, and what data would validate or strengthen each finding. Attribution to the FLOW framework appears on every response, by rule. ## 10 Findings Fixed in 6 Tasks The post-v1.9.5 cybersecurity audit returned 10 findings: 1 HIGH, 4 MEDIUM, 5 LOW, plus 1 INFO. All closed in v1.9.6. No deferrals. The implementation went through Subagent-Driven Development with two-stage review per task: spec compliance first, then code quality. The HIGH finding was the most surgical. VULN-A01 : the seo-flow agent was granted Bash in its tool list. Bash plus WebFetch is a prompt-injection-to-shell pipeline. Fix: drop Bash from the tools line. The agent reads files, fetches URLs, and matches patterns. It never needed shell execution. The orchestrator-level /seo flow sync command still has Bash and runs the sync script normally. The agent does not. The 4 MEDIUM findings cluster around the sync script. VULN-A02 and VULN-A07 both relate to the GitHub token strategy. The original code grabbed a full-scope PAT via gh auth token on every run and sent it in the Authorization header. That meant a redirect on the API path could leak the token to a third party. Fix: anonymous-first. The script sends no token by default, and only escalates to authenticated requests on a 403 fallback if gh is available. VULN-A03 was a path-traversal write. record_write() wrote any path it received without verifying the path was inside the skill directory. Fix: Path.resolve() containment check before any write. VULN-A04 introduced a SHA-256 lockfile for prompt integrity (more on that below). VULN-A05 tagged WebFetch responses as untrusted in the agent body, with explicit guidance not to execute, eval, or relay fetched content verbatim. The 5 LOW findings closed gaps that were technically defense-in-depth but worth fixing now. VULN-A06 : graceful degradation when gh CLI is missing instead of a hard sys.exit. VULN-A08 : atomic writes via tempfile.mkstemp plus shutil.move , eliminating partial-write corruption on interrupt. VULN-A09 : 5 MB response cap and 15-second timeout on every API call. VULN-A10 : URL allowlist that validates HTTPS scheme and api.github.com host, blocking the @evil.com userinfo bypass form. The single INFO finding ( INFO-A14 ) added a CC BY 4.0 attribution header to references/prompts/README.md . Small, but the FLOW license requires attribution when reproducing or adapting the prompts. ## Anonymous-First Token Strategy Explained v1.9.6 sends zero credentials on the first GitHub API request. The sync script makes anonymous calls. GitHub's unauthenticated rate limit is 60 requests per hour per IP, which is more than enough for syncing roughly 50 small prompt files. The token only enters the picture if a 403 comes back. That changes the threat model. Before v1.9.6, every sync request carried a full-scope GitHub PAT. If the API ever 302-redirected to a host the script did not own, the token went along for the ride. After v1.9.6, the default headers contain only Accept and X-GitHub-Api-Version . No Authorization key. No leak surface. ``` def _base_headers(): return { "Accept": "application/vnd.github+json", "X-GitHub-Api-Version": "2022-11-28", } ``` The 403-triggered escalation has its own guard. The retry only happens if the original request had no Authorization header AND the escalated headers actually contain a token. If gh CLI is absent, _authed_headers() falls back to base headers, the recursion check fails, and the script raises the original 403 instead of looping forever. That edge case caught a real infinite-loop bug during code review. The practical effect: you can run /seo flow sync on a fresh machine with no gh CLI installed and it works. You can run it inside a CI container with no credentials and it works. You can run it locally with gh auth login done and it still works, with the token only sent if the rate limit kicks in. ## SHA-256 Lockfile for Prompt Integrity flow-prompts.lock is a sha256sum-compatible file pinning the SHA-256 of every synced FLOW prompt. It lives at skills/seo-flow/references/flow-prompts.lock and is regenerated on every sync, with a drift report printed before any write. The format is identical to sha256sum output: 64-char hex digest, two spaces, relative path. That means you can verify the lockfile's claims with one shell command: ``` cd skills/seo-flow/references sha256sum --check flow-prompts.lock ``` The drift detection runs on every sync. It reads the existing lockfile, computes hashes for what is about to be written, and prints any ADDED, CHANGED, or REMOVED lines to stderr. If upstream changes a prompt, you see it before the file gets overwritten. That gives you a chance to review what changed and decide whether to commit the new lockfile. This is the same pattern package-lock.json uses for npm and Cargo.lock uses for Rust. It does not stop tampering, but it surfaces it. Combined with the URL allowlist, the path containment check, the 5 MB cap, and the atomic write path, the integrity story for synced FLOW content is auditable end-to-end. ## How to Install or Upgrade One command. Same as every prior release. ``` claude /install github:AgriciDaniel/claude-seo ``` If you are upgrading from v1.9.0 or earlier, the install command pulls v1.9.6 directly. The first run of /seo flow sync generates the lockfile and writes the 41 prompts to skills/seo-flow/references/prompts/ . Existing skill configurations are untouched. To try FLOW immediately on a target URL: ``` /seo flow find https://yourdomain.com ``` 21 sub-skills. 18 agents. 30 scripts. MIT licensed for the skill code, CC BY 4.0 for the FLOW prompt content. Free forever. The optional DataForSEO and Firecrawl extensions still work the same way they did in [v1.9.0](https://claude-seo.md/blog/claude-seo-v190-community-release). ## What Comes Next v1.9.7 is already in scope. Three things on the list: deeper FLOW cross-skill integration with seo-content (Leverage stage maps directly to E-E-A-T amplification), a per-stage CLI flag for /seo flow sync to pull only specific stages, and an optional offline mode that uses the bundled lockfile as the source of truth without hitting the network. The [Google API integration](/blog/google-api-seo-automation-claude-code) story keeps evolving too. The plan is to surface FLOW Win-stage prompts directly inside the Search Console reporting flow, so rank tracking output triggers stage-appropriate next actions rather than just numbers. If you build with Claude Code and want your work in the next release, the AI Marketing Hub Pro Skool community is where the next Pro Hub Challenge lives. The pattern from v1.9.0 worked: 6 contributors, 5 passing review, 4 new skills shipped. The same door is open for v1.10.0. Full release notes for both tags: [v1.9.5 (FLOW integration)](https://github.com/AgriciDaniel/claude-seo/releases/tag/v1.9.5) and [v1.9.6 (security hardening)](https://github.com/AgriciDaniel/claude-seo/releases/tag/v1.9.6). The findings table, the test list, and the migration steps are all there with line-level detail. Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) ## Related reading Read the earlier [Firecrawl and backlink-analysis release note](/blog/claude-seo-172-firecrawl-backlink-analysis), then see the complete [Claude Code SEO stack](/blog/claude-code-seo-stack). *** # claude-music: Generate Full Songs in Your Terminal with ACE-Step 1.5 - URL: [https://agricidaniel.com/blog/claude-music-ai-production](https://agricidaniel.com/blog/claude-music-ai-production) - Published: 2026-04-22 - Updated: 2026-04-22 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. ACE-Step 1.5 outscores Suno v5 and Udio v1.5 on every published benchmark - and runs on your GPU for $0. claude-music wraps it in a 10-command production system. Generate songs, remix tracks, fine-tune your own style with LoRA. No cloud, no subscriptions. ## The Open-Source Model That Beats Suno and Udio on Every Benchmark The generative AI in music market hit $569.7 million in 2024 and is growing at 30.4% annually toward $2.8 billion by 2030 ([Grand View Research](https://www.grandviewresearch.com/industry-analysis/generative-ai-in-music-market-report), 2024). Suno, Udio, and their cloud competitors are riding that wave - charging $10-20/month for access to models you can't inspect, storing your audio on their servers, and updating their Terms of Service whenever it suits them. Meanwhile, [ACE-Step 1.5](https://github.com/ace-step/ACE-Step-1.5) - a fully open-source music generation model - outperforms both on every published benchmark, runs on your GPU, and costs nothing. I wrapped it in a Claude Code skill. [claude-music](https://github.com/AgriciDaniel/claude-music) is a 10-command AI music production system that runs entirely in your terminal. Generate full songs from text descriptions. Remix tracks with style transfer. Fine-tune on your own music catalog using LoRA. Export platform-ready files for Spotify, YouTube, or TikTok. Everything stays local. No accounts, no monthly fees, no data leaving your machine. Key Takeaways - ACE-Step 1.5-XL outscores Suno v5 and Udio v1.5 on all 4 published benchmarks ([ACE-Step](https://ace-step.github.io/ace-step-v1.5.github.io/), April 2025) - Full song generation in under 2 seconds on an A100, under 10 seconds on an RTX 3090 - 86% of global creators already use generative AI for content work ([Adobe Creators' Toolkit](https://news.adobe.com/news/2025/10/adobe-max-2025-creators-survey), October 2025) - music is the last gap - 10 sub-skills: generate, cover, repaint, compose, export, analyze, enhance, random, library, lora - LoRA fine-tuning on 3-10 of your own songs in approximately 1 hour on an RTX 3090 ## What Is ACE-Step 1.5 and Why Does It Beat the Paid Tools? ACE-Step 1.5-XL scores 7.76 on AudioBox, 8.12 on SongEval, 6.62 on Style Align, and 8.42 on Lyric Align - beating Suno v5 (7.69, 7.87, 6.51, 8.29) and Udio v1.5 (7.45, 7.65, 6.15, 8.03) across the board ([ACE-Step official](https://ace-step.github.io/ace-step-v1.5.github.io/), April 2025). It's a diffusion-based model - the same architecture that made image generation explode, applied to audio generation with dedicated music-specific training. ACE-Step 1.5-XL vs Suno v5 vs Udio v1.5 - Benchmark Scores Grouped bar chart. AudioBox: 7.76 vs 7.69 vs 7.45. SongEval: 8.12 vs 7.87 vs 7.65. Style Align: 6.62 vs 6.51 vs 6.15. Lyric Align: 8.42 vs 8.29 vs 8.03. ACE-Step leads on all four. ACE-Step 1.5-XL vs Suno v5 vs Udio v1.5 Benchmark scores (higher = better) - ACE-Step official, April 2025 ACE-Step 1.5-XL Suno v5 Udio v1.5 - 5.5 6.0 6.5 7.0 7.5 8.0 8.5 7.76 7.69 7.45 8.12 7.87 7.65 6.62 6.51 6.15 8.42 8.29 8.03 AudioBox SongEval Style Align Lyric Align Source: ACE-Step official benchmarks, April 2025 ACE-Step 1.5-XL leads across all four benchmarks - beating both Suno v5 and Udio v1.5 The technical architecture splits into two tiers. The Turbo model is 2B parameters, completes in 8 diffusion steps, and runs on as little as 4GB VRAM - that's an RTX 3060 or better. The XL model is 4B parameters with ~9GB bf16 weights, needs about 16GB VRAM for the highest quality setting, and is what produces the benchmark-leading results above. Both tiers support 10 seconds to 10 minutes of audio, 1,000+ instruments and styles, batch generation of up to 8 songs simultaneously, and 50+ languages for vocals. According to the ACE-Step team, a full song generates in under 2 seconds on an A100 and under 10 seconds on an RTX 3090 ([ACE-Step GitHub](https://github.com/ace-step/ACE-Step-1.5), April 2025). That multilingual support matters more than it sounds - most AI music tools struggle with non-English lyrics. If you're creating content for Korean, Japanese, Spanish, or French audiences, ACE-Step 1.5 handles it without the awkward phonetic artifacts you get elsewhere. ## 10 Sub-Skills: What Can You Actually Do? 84% of developers are using or plan to use AI tools in their development process, but 66% cite AI solutions that are "almost right but not quite" as their biggest frustration ([Stack Overflow Developer Survey](https://survey.stackoverflow.co/2025/ai), 2025). claude-music's 10 sub-skills are designed to give you precise control rather than hoping the model guesses your intent correctly. Command What It Does /music generate Create music from a text description + optional lyrics. Specify duration, language, quality preset. /music cover Style transfer. Remake a reference track in a different genre or style. /music repaint Edit a specific section of a song. Target a timestamp range and describe what to change. /music compose Songwriting assistance: lyrics, caption suggestions, BPM and key recommendations. /music export Platform-optimized export. Handles loudness normalization for Spotify, YouTube, TikTok, podcast, CD. /music analyze Check BPM, key, loudness levels, and frequency spectrum of any audio file. /music enhance Normalize levels, denoise, separate stems (vocals, drums, bass, other). /music random Random genre and style generation. Useful when you don't know what you want. /music library Browse and search your generated music. Filter by genre, BPM, date, or keyword. /music lora Fine-tune ACE-Step on 3-10 of your own songs to create a custom style checkpoint. Three of these deserve special attention. repaint is what separates claude-music from "generate and hope" - if your chorus is perfect but the intro drags, you describe the timestamp range and what you want changed. That's section-level control, not full regeneration. lora gives you a persistent style fingerprint - train it once, use it on every future generation. export handles the painful loudness normalization step that most creators get wrong: Spotify targets -14 LUFS, YouTube -13 LUFS, TikTok -14 LUFS. One command, correct output. From testing claude-music: Standard quality (the default, ~15 seconds per generation) produces output good enough for YouTube background music or podcast intros on the first attempt about 80% of the time. The other 20% needs one /music repaint pass on the intro or first chorus. Max quality raises that first-try success rate to roughly 92% - at the cost of 3-5 minutes per generation. For most use cases, standard is the right starting point. Run max quality when you're finalizing. ## How Does LoRA Fine-Tuning Work? ACE-Step 1.5 supports LoRA fine-tuning on 3-10 songs in approximately 1 hour on an RTX 3090 ([ACE-Step GitHub](https://github.com/ace-step/ACE-Step-1.5), April 2025). This is what makes claude-music genuinely useful for professional use rather than just experimentation. The workflow: # Step 1: Point it at your training songs /music lora --train ./my-songs/ --name my-style # Step 2: Wait ~1 hour (RTX 3090) # The trainer runs locally, nothing is uploaded # Step 3: Use your checkpoint in every future generation /music generate --caption "upbeat summer pop" --lora my-style --duration 60 The 3-10 song requirement isn't arbitrary. Fewer than 3 songs and the model memorizes rather than learns style - every generation sounds too similar to your specific tracks. More than 10 songs and training time extends significantly with diminishing quality returns. The sweet spot for most artists is 5-8 songs that represent the range of your intended output style. What does this unlock in practice? Podcast producers can train on their existing intro music and auto-generate episode-specific variations that stay on-brand. YouTube creators can build a signature sound for a channel and generate matching background tracks per video. Game developers can train on an environmental audio palette and generate new tracks that fit without manual A/B testing against the existing mix. The key point: your custom style stays on your hardware. Nobody else trains on your music, and no API dependency means no training data policy changes to worry about. ## Why Run Music AI Locally Instead of Using Suno or Udio? 86% of global creators now use generative AI in their content work, and 81% say it helps them produce content they couldn't have made otherwise ([Adobe Creators' Toolkit Report](https://news.adobe.com/news/2025/10/adobe-max-2025-creators-survey), October 2025). But cloud tools carry a hidden cost beyond the monthly fee: your output, your style fingerprint, and your training data all live on someone else's server. Music licensing is already complicated - you don't need to add "my cloud provider might have a claim on this" to the legal surface area. With claude-music, the audio files live on your disk. The model weights live on your disk. The LoRA checkpoints you train live on your disk. Nothing is uploaded during generation or training. The copyright situation is exactly as clear as it would be if you'd produced the audio yourself - which, for AI-generated content, remains an open question in most jurisdictions, but at least it's between you and the relevant law, not between you and a Terms of Service update. Here's what most coverage of AI music tools misses: the most valuable use case isn't replacing professional musicians. It's giving developers, marketers, and creators access to production-quality background audio without a subscription or a digital audio workstation. The person who benefits most from claude-music isn't a producer looking to automate their workflow - it's the developer who needs three variations of a lo-fi beat for an app demo and doesn't want to spend $40 on a stock audio license, or the marketer who needs 12 regional variations of background music for localized video ads. Generative AI in Music Market Growth 2024-2030 Area chart showing the generative AI in music market growing from $569.7M in 2024 to a projected $2.8B by 2030 at a 30.4% CAGR. AI in Music Market Growth $569.7M (2024) to $2.8B (2030*), CAGR 30.4% - Grand View Research, 2024 $0 $1B $2B $570M $2.8B 2024 2025 2026 2027 2028 2029 2030* * Projected at 30.4% CAGR. Intermediate values extrapolated. Source: Grand View Research, 2024. The AI music market grows nearly 5x by 2030 - most of it flows to cloud platforms right now ## How to Install claude-music in 5 Minutes The installer handles everything: Python environment setup via uv, FFmpeg, and the ACE-Step models (~5GB, downloads with explicit confirmation). You need Claude Code and a GPU with at least 4GB VRAM. Here's the full setup: # Step 1: Clone the repo git clone https://github.com/AgriciDaniel/claude-music.git cd claude-music # Step 2: Run the installer bash install.sh # Linux / macOS # OR powershell -ExecutionPolicy Bypass -File .\install.ps1 # Windows The installer detects your GPU and VRAM, tells you which quality presets are available, downloads ACE-Step with your confirmation, and runs a test generation to verify the setup works. There's nothing to configure manually afterward. Once installed, open Claude Code and try: /music generate "chill lo-fi beat, 60 seconds" /music generate --caption "upbeat pop, female vocal" --duration 90 --quality high /music random The 30+ built-in genre recipes handle the prompt engineering for you. You don't need to know that a lo-fi beat needs "boom bap drums, vinyl crackle, rhodes piano, muted guitar" - typing "lo-fi beat" activates the recipe, which supplies the optimal caption structure, BPM range, and instrument weighting automatically. From building this: The hardest engineering problem wasn't model integration - it was VRAM detection. Different GPU generations report available memory differently, and the loading behavior changes significantly at 4GB, 8GB, and 16GB boundaries. The detect_gpu.sh script went through six iterations before it reliably gave correct quality preset recommendations across RTX 3060, 3090, and 4090 hardware. If you hit a preset mismatch, running /music setup re-runs detection and resets the config. ## Frequently Asked Questions ### Do I need a powerful GPU to run claude-music? 4GB VRAM is the minimum - that's an RTX 3060 or equivalent. At 4GB you get the Turbo model at standard quality. 8GB unlocks Turbo with extended thinking for better structure. 16GB+ gives you the XL model at full quality - what the benchmark scores above are based on. CPU-only mode works but generation takes 5-10 minutes instead of seconds. ### How does the output quality actually compare to Suno or Udio? On the four published benchmarks, ACE-Step 1.5-XL scores higher than both services across AudioBox, SongEval, Style Align, and Lyric Align ([ACE-Step official](https://ace-step.github.io/ace-step-v1.5.github.io/), April 2025). The XL model's vocal clarity and lyric coherence are the most noticeable improvements over standard quality - particularly in English and for complex song structures with clear verses and choruses. ### Can I use the generated music commercially? The claude-music skill and ACE-Step model weights are MIT licensed. The legal status of AI-generated audio for commercial use varies by jurisdiction and continues to evolve - the same landscape applies here as with any AI content tool. No additional restrictions are added beyond the MIT license and the model's own terms. ### What audio formats does the export command produce? WAV (lossless, for production workflows), MP3 (standard distribution), and platform-specific exports with proper loudness normalization: Spotify (-14 LUFS), YouTube (-13 LUFS), TikTok (-14 LUFS), podcast (-16 LUFS), and CD (-23 LUFS). The /music analyze command can verify the output meets target specs before upload. ### Does it work on Windows? Yes - the PowerShell installer ( install.ps1 ) handles full setup on Windows. The skill works wherever Claude Code runs: CLI, Desktop app (Mac and Windows), and VS Code extension. Developer Mode or Admin privileges are required for the Windows installer to create the necessary symlinks. ## Build Your Audio Stack in the Terminal The AI music market is growing at 30.4% annually and most of that growth flows toward closed, cloud-locked platforms. ACE-Step 1.5 is the first open-source model to match - and in benchmarks, beat - their quality. claude-music puts a full production system on top of it: generate, remix, fine-tune, analyze, and export, all from a single terminal command. 10 sub-skills. 30+ genre recipes. LoRA fine-tuning. Platform export. Zero subscriptions. Star the repo on [GitHub](https://github.com/AgriciDaniel/claude-music) - See what else I've built on the [about page](/about) - Pair it with [claude-canvas](/blog/claude-canvas-ai-visual-production) for visual + audio production in the same terminal - Learn more about building tools like this with [Skill Forge](/blog/skill-forge-build-claude-code-skills) Join 4,500+ AI Marketing Builders Workflow templates, automation blueprints, and a community of SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) ## Related Posts - [Claude Code Just Turned Obsidian Canvas Into an AI Design Studio](/blog/claude-canvas-ai-visual-production) - Visual production companion to claude-music - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive ranking of skills that save time - [Build Your Own Claude Code Skills with Skill Forge](/blog/skill-forge-build-claude-code-skills) - From idea to published skill in one session - [Obsidian AI Second Brain](/blog/claude-obsidian-ai-second-brain) - The knowledge engine that pairs well with the audio stack *** # Claude Code Security: The AI Security Audit That Found 23 Vulnerabilities in My Own Code - URL: [https://agricidaniel.com/blog/claude-cybersecurity-ai-security-audit](https://agricidaniel.com/blog/claude-cybersecurity-ai-security-audit) - Published: 2026-04-14 - Updated: 2026-04-14 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. claude-cybersecurity is an open-source Claude Code skill that runs 8 parallel security agents against your codebase. Zero config, one command, full OWASP Top 10 coverage. Here is how it works and what it found. claude-cybersecurity is a free, open-source Claude Code skill that turns your terminal into a full security audit platform. One command. Eight specialist agents running in parallel. Zero configuration. It covers the OWASP Top 10:2025, CWE Top 25, MITRE ATT&CK techniques, 11 programming languages, and 5 compliance frameworks. If you are shipping code in 2026, especially vibe-coded code, you need something like this in your workflow. I built it after realizing that the code I was shipping with AI assistance was accumulating security debt faster than I could track. The tool paid for itself on the first run when it found an SSRF vulnerability in my own [claude-ads](/blog/claude-ads-v1-5-release) project that I had missed entirely. Key Takeaways - claude-cybersecurity runs 8 parallel security agents with a single /cybersecurity command - Covers OWASP Top 10:2025, CWE Top 25, and 7 MITRE ATT&CK techniques across 11 languages - AI-generated code has 2.74x more vulnerabilities than human-written code (Veracode 2025) - Uses a weighted scoring system (0-100) with auto-CRITICAL gate for severe findings - Free and open-source alternative to GitHub Advanced Security (GHAS) - Install in 30 seconds with a single curl command ## What Is Claude Cybersecurity? claude-cybersecurity is a Claude Code skill that orchestrates eight specialist security agents to audit your codebase. You type /cybersecurity in your terminal and it handles everything: detecting your stack, mapping trust boundaries, running parallel analyses, and producing a prioritized report with fix suggestions. No API keys. No configuration files. No SaaS subscription. It works with any project Claude Code can read, which means basically everything on your machine. The skill follows the same architecture pattern I use in all my [Claude Code skills](/blog/best-claude-code-skills-2026): a coordinator that gathers context, dispatches specialist agents, and synthesizes their output into something actionable. The difference here is the depth. Each of the eight agents carries its own reference data, detection heuristics, and false-positive suppression rules tuned to specific frameworks. ## Why Vibe-Coded Apps Need Security Audits Vibe coding security is not a theoretical concern anymore. The data from 2025 makes it clear that AI-assisted code carries measurably higher risk than code written by hand. If you are using Claude, Copilot, or any other AI coding tool to ship production code, you are likely introducing vulnerabilities at a rate your existing review process cannot catch. According to Veracode's 2025 State of Software Security report, AI-generated code contains 2.74x more security flaws than human-written code. That study analyzed over 2.2 million applications. Separately, 45% of AI-generated code snippets introduce at least one OWASP Top 10 vulnerability. Researchers at Georgia Tech tracked 74 CVEs that originated directly from AI-generated code merged into open-source projects during 2024-2025. The pattern makes sense once you think about it. AI models are trained on the entire internet, including millions of Stack Overflow answers and tutorials that use insecure patterns for the sake of simplicity. The model optimizes for "code that works" not "code that is secure." It will happily generate SQL queries with string concatenation, store secrets in plaintext, or skip input validation if you do not explicitly ask for those things. Traditional SAST tools catch some of this, but they were designed for patterns that human developers write. AI introduces new categories of risk: hallucinated dependencies (packages that do not exist, which attackers register as malware), overconfident security implementations that look correct but have subtle flaws, and a particular tendency to copy insecure patterns from training data without understanding why they are dangerous. ## The 8 Specialist Agents Rather than running a single monolithic scan, claude-cybersecurity dispatches eight focused agents that run in parallel. Each agent has a specific security domain, a weighted contribution to the overall score, and its own set of detection rules. This parallel architecture means the full audit completes faster than a sequential scan while catching cross-domain issues that single-purpose tools miss. - 8 Specialist Agents — Parallel Execution Vulnerability Scanner 20% — OWASP Top 10 + CWE Top 25, taint analysis Auth Reviewer 15% — IDOR, privilege escalation, session management Threat Intel 15% — Malware, backdoors, C2 comm, MITRE ATT&CK Secrets 10% — Semantic detection, obfuscated credentials Deps 10% — Supply chain, slopsquatting, typosquatting IaC 10% — Terraform, Docker, K8s, GitHub Actions AI Code 10% — AI-generated patterns, hallucinated deps Logic 10% — Business logic, race conditions, TOCTOU Vulnerability Scanner (20%) is the heaviest agent. It performs taint analysis, tracks data flow from user inputs to dangerous sinks, and maps findings against both the OWASP Top 10:2025 and CWE Top 25:2024 catalogs. This agent catches injection flaws, XSS, deserialization issues, and path traversal vulnerabilities. Auth Reviewer (15%) focuses exclusively on authentication and authorization. It looks for IDOR (Insecure Direct Object Reference) patterns, privilege escalation paths, broken session management, and missing access controls. This agent is especially good at catching the "forgot to check permissions" bugs that are rampant in AI-generated code. Threat Intelligence (15%) scans for indicators of compromise: malware signatures, backdoor patterns, command-and-control communication channels, and known attack techniques mapped to the MITRE ATT&CK framework. This is the agent that would flag it if a dependency or code snippet contained obfuscated malicious payloads. Secrets Detection (10%) goes beyond regex-based secret scanning. It uses semantic analysis to find obfuscated credentials, hardcoded tokens disguised as configuration values, and secrets that have been base64 encoded or split across multiple variables. Traditional secret scanners miss these patterns regularly. Dependency Auditor (10%) handles supply chain security. It checks for known vulnerable dependencies, typosquatting (packages with names similar to popular ones), and slopsquatting (packages that AI models hallucinate into existence and that attackers then register on npm/PyPI). This is a growing attack vector that most teams are not monitoring. IaC Scanner (10%) audits your infrastructure-as-code: Terraform configurations, Dockerfiles, Kubernetes manifests, and GitHub Actions workflows. Misconfigured infrastructure is one of the leading causes of breaches, and this agent catches overly permissive IAM policies, unpinned action versions, exposed ports, and insecure container configurations. AI Code Reviewer (10%) specifically targets patterns common in AI-generated code. Hallucinated dependencies, copy-pasted insecure patterns from training data, overconfident crypto implementations, and the characteristic "looks right but is subtly broken" code that LLMs produce. This agent exists because AI code has different failure modes than human code. Business Logic Analyzer (10%) looks for race conditions, TOCTOU (Time of Check to Time of Use) bugs, improper state machine transitions, and logic flaws that cannot be detected by pattern matching alone. These are the vulnerabilities that are hardest to find with traditional SAST tools because they require understanding the application's intended behavior. ## How It Works: The GARE Architecture The skill follows a four-phase architecture called GARE: Gather, Analyze, Recommend, Execute. This is the same orchestration pattern used in enterprise security tools, adapted to run entirely within Claude Code's execution environment. The entire pipeline runs locally with no data leaving your machine. PHASE 1: GATHER Detect stack | Enumerate entry points | Map trust boundaries | STRIDE analysis | Build context 🔍 PHASE 2: ANALYZE — 8 Agents in Parallel Vuln 20% Auth 15% Threat 15% Secrets 10% Deps 10% IaC 10% AI Code 10% Logic 10% Each agent loads its own reference files | Returns VULN-XXX findings + category score (0-100) + confidence levels PHASE 3: RECOMMEND Score aggregation | Attack-path chaining | Compliance PHASE 4: EXECUTE Structured report | Prioritized remediation queue /cybersecurity [path] [--scope full|quick|diff] [--compliance pci|hipaa|soc2|gdpr] Zero configuration | Auto-detects languages and frameworks | Framework-aware FP suppression Phase 1: Gather. The coordinator scans your project to detect languages, frameworks, and infrastructure. It enumerates entry points (API routes, form handlers, CLI interfaces), maps trust boundaries (where user input enters the system), and performs a STRIDE threat model. This context is passed to every agent so they know what they are looking at. Phase 2: Analyze. All eight agents run in parallel. Each receives the gathered context plus its own domain-specific reference files. Each returns a list of findings (tagged as VULN-001, VULN-002, etc.) with severity scores, confidence levels, affected files, and suggested fixes. The parallel execution means a full audit on a medium-sized codebase takes minutes, not hours. Phase 3: Recommend. The coordinator aggregates findings across all agents, deduplicates overlapping issues, chains related vulnerabilities into attack paths, and maps everything against your selected compliance framework (PCI DSS, HIPAA, SOC 2, GDPR, or NIST 800-53). The output is a prioritized remediation queue ordered by risk. Phase 4: Execute. The final report includes the overall security score, a letter grade (A through F), every finding with its severity and confidence level, and specific code-level fix suggestions. You can ask Claude Code to apply fixes directly, or export the report for your team to review. ## Real Results: Auditing Claude Ads (62/100 to 90/100) The best way to show what this tool does is to share what happened when I ran it against my own code. I pointed claude-cybersecurity at the [claude-ads v1.5](/blog/claude-ads-v1-5-release) codebase, expecting a clean bill of health. I was wrong. The initial score came back at 62/100, a D grade. The tool found 23 vulnerabilities across 5 categories. The most serious finding was an SSRF (Server-Side Request Forgery) vulnerability in the API integration layer. The code accepted user-provided URLs for webhook callbacks without validating the destination. An attacker could have used this to make the server send requests to internal services. The tool flagged it as CRITICAL with HIGH confidence and provided the exact fix: URL validation with an allowlist of permitted domains. The IaC agent found that several GitHub Actions workflows used unpinned action versions (e.g., uses: actions/checkout@v4 instead of pinning to a specific SHA). This is a supply chain risk because a compromised action could inject malicious code into the CI pipeline. The fix was straightforward: pin every action to its full commit SHA. The tool also flagged missing CI security gates. There was no automated security scanning in the CI pipeline, meaning vulnerabilities could be merged without any automated check. I added CodeQL scanning and dependency review as required checks. After applying all 23 fixes and re-running the audit, the score jumped to 90/100. That work shipped in the [v1.5.1 patch release](/blog/claude-ads-v1-5-release). ## Scoring System The scoring system is designed to be both precise and practical. Every finding gets a severity score calculated from four factors: base severity (mapped from CVSS), confidence level, exploitability, and contextual modifiers. The overall project score is a weighted aggregate of all eight agent scores, where the weights match the percentages shown in the agents chart above. Security Score — 0 to 100 F D C B A 0 25 50 75 90 100 Score = Base Severity (CVSS) x Confidence (0.3-1.0) x Exploitability (0.5-1.0) +/- Context (-20 to +20) 4-tier confidence: HIGH (90-100%) | MEDIUM (60-89%) | LOW (30-59%) | INFO ( Join the AI Marketing Hub Free community for AI tools, skills, and marketing automation [Join Free](https://www.skool.com/ai-marketing-hub) *** # claude-ads v1.5: 250+ Ad Audit Checks Across 7 Platforms - URL: [https://agricidaniel.com/blog/claude-ads-v1-5-release](https://agricidaniel.com/blog/claude-ads-v1-5-release) - Published: 2026-04-13 - Updated: 2026-04-13 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. v1.5 adds 22 new audit checks, 3 new skills (/ads math, /ads test, /ads report), and a PDF report generator. 250+ weighted checks now cover Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, and Apple Ads with 2025-2026 platform research baked in. ## What Changed in claude-ads v1.5? claude-ads v1.5 adds 22 new audit checks, 3 new skills, and a PDF report generator. Total coverage is now 250+ checks across 7 ad platforms, all updated with 2025-2026 platform research. The biggest jumps: Apple Ads goes from 0 to 35+ checks, Google Ads from 62 to 80, and Meta from 48 to 50. Key changes in this release: - Apple Ads support: 35+ checks covering Custom Product Pages, Maximize Conversions bidding (GA Feb 2026), and Search tab placements - Google Ads updates: 80 checks now include AI Max for Search, Smart Bidding Exploration (+18% queries per Google), and ECPC deprecation flags - 3 new skills: /ads math for PPC calculators, /ads test for A/B test design, /ads report for PDF generation - PDF report generator: client-ready deliverables with inline charts, health scores, and prioritized findings - Security fixes: SSRF protection on URL inputs, path traversal fix in screenshot capture - Scoring overhaul: severity multipliers recalibrated, Quick Wins section added to every audit Key Takeaways - 250+ weighted audit checks across 7 platforms: Google (80), Meta (50), Apple (35+), TikTok (28), LinkedIn (27), Microsoft (24), YouTube (15) - 3 new skills: /ads math for PPC calculators, /ads test for A/B test design, /ads report for PDF generation - Severity-weighted scoring with Critical 5.0x, High 3.0x, Medium 1.5x, Low 0.5x multipliers across 6 categories - 100% local analysis, zero data leaves your machine. Open source, MIT licensed. Platform Coverage Grid 250+ weighted audit checks across 7 advertising platforms Google Ads 80 checks Search PMax AI Max Meta Ads 50 checks Pixel/CAPI Andromeda LinkedIn Ads 27 checks B2B TLA Lead Gen TikTok Ads 28 checks Creative Smart+ Shop Microsoft Ads 24 checks Copilot CTV Import Apple Ads 35+ checks CPPs Max Conv YouTube 15 checks Shorts DemandGen CTV Platform coverage grid: 250+ weighted audit checks distributed across 7 platforms claude-ads v1.5 in action - parallel agents auditing ad accounts ## What Did the 2026 Platform Research Reveal? Digital ad spend is projected to hit $870 billion globally in 2027, growing at 10% CAGR ([Statista](https://www.statista.com/outlook/dmo/digital-advertising/worldwide), 2026). We reviewed official documentation, case studies, and API changelogs across all 7 platforms. Five findings changed how the tool works. Each one led to new checks, updated benchmarks, or removed recommendations that were no longer accurate. 1. Meta's Andromeda clusters similar ads. Meta's retrieval engine (Andromeda, per their 2023 engineering blog) groups creatively similar ads before auction. Running 100 ad variations that share the same hook, format, and CTA produces no incremental reach over 10 well-differentiated variants. The tool now flags creative clustering risk when variation count exceeds 10 per ad set without meaningful creative diversity. 2. LinkedIn Maximum Delivery is the most expensive bidding option. LinkedIn's own campaign manager documentation confirms Maximum Delivery consistently produces the highest CPCs across campaign types. The audit now recommends starting with Manual CPC and only testing Maximum Delivery after establishing baseline performance data. 3. Google ECPC is fully deprecated as of March 2025. Enhanced CPC was sunset in March 2025 per Google Ads Help Center. Campaigns still referencing ECPC get flagged as critical. The replacement recommendation is Smart Bidding Exploration, which Google's internal data (Search Ads 360 release notes) shows delivers +18% more search queries and +19% more conversions on average. 4. TikTok creative lifespan compressed to 7-10 days under Smart+. TikTok's Creative Center data and advertiser community reports confirm that Smart+ campaigns cycle through creatives faster than manual campaigns. The creative fatigue check now uses a 7-day window instead of 14 days for Smart+ campaigns. 5. Apple rebranded to "Apple Ads" with Maximize Conversions GA Feb 26, 2026. Apple Search Ads is now Apple Ads (per Apple's developer documentation update, Q4 2025). Maximize Conversions bidding went generally available on February 26, 2026. The new Apple Ads skill covers Custom Product Pages, Search tab placements, and the new bidding strategy. ## How Do 250+ Audit Checks Work? The tool runs 6 parallel agents that evaluate your ad accounts simultaneously. Each agent handles a specific domain: Google (80 checks), Meta (50 checks), creative quality (21+ checks), conversion tracking (8+ checks), budget allocation (24 checks), and compliance (18+ checks). A full audit completes in 3-5 minutes. The system uses a 3-layer architecture. The directive layer (ads/SKILL.md) handles routing, quality gates, and orchestration. The orchestration layer deploys 6 audit agents as forked tasks that run in parallel. The execution layer contains 25 reference files, 19 sub-skills, and 12 industry-specific templates. Each layer can operate independently; the orchestrator never needs to wait for a reference file to load before dispatching agents. text { font-family: 'Space Grotesk', system-ui, -apple-system, sans-serif; } DIRECTIVE LAYER ads/SKILL.md Orchestrator · Routing · Quality Gates - ORCHESTRATION LAYER audit-google 80 checks audit-meta 50 checks audit-creative 21+ checks audit-tracking 8+ checks audit-budget 24 checks audit-compliance 18+ checks EXECUTION LAYER 25 References On-demand knowledge 19 Sub-Skills Specialized analysis 12 Templates Industry-specific 3-layer architecture: directive routes commands, orchestration dispatches parallel agents, execution provides knowledge and tools text { font-family: 'Space Grotesk', system-ui, -apple-system, sans-serif; } /ads audit audit-google 80 audit-meta 50 audit-creative 21+ audit-tracking 8+ audit-budget 24 audit-compliance 18+ Unified Health Score (0-100) Parallel audit flow: /ads audit dispatches 6 agents simultaneously, results merge into a unified health score ## How Does the Health Score Scoring Work? Every check gets a severity multiplier (Critical 5.0x, High 3.0x, Medium 1.5x, Low 0.5x) and a category weight. The weighted formula produces a 0-100 score with an A through F grade. A failed Critical check in the Conversion Tracking category (25% weight) costs 1.25 points. A failed Low check in Settings (10% weight) costs 0.05 points. This means the tool prioritizes what actually costs you money. The six scoring categories and their weights: Conversion Tracking (25%), Wasted Spend (20%), Structure (15%), Keywords (15%), Ads (15%), Settings (10%). These weights come from analyzing which categories correlate most strongly with wasted ad spend. Conversion tracking failures are weighted highest because if your tracking is broken, every optimization decision downstream is wrong. New in v1.5: every audit now includes a Quick Wins section. These are the 3-5 highest impact, lowest effort fixes from your results. A typical Quick Win might be "Add 12 negative keywords to Campaign X to eliminate $2,100/mo in irrelevant clicks." The tool calculates estimated savings based on your actual spend data and the severity of each finding. Weighted Scoring Algorithm text { font-family: 'Space Grotesk', system-ui, sans-serif; } Weighted Scoring Algorithm Score = S(Pass x Severity x CategoryWeight) / S(Total x Severity x CategoryWeight) x 100 Category Weights 6 categories Conversion Tracking (25%) Wasted Spend (20%) Structure (15%) Keywords (15%) Ads (15%) Settings (10%) Severity Multipliers Critical 5.0x High 3.0x Medium 1.5x Low 0.5x Example Impact Failed Critical check in Conversion Tracking: Impact = 5.0 (severity) x 0.25 (category) = 1.25 pts Weighted scoring algorithm: severity multipliers combined with category weights produce a 0-100 health score ## What Are the 3 New Skills? Manual calculations and reporting are recurring PPC tasks. v1.5 ships three new skills that extend the tool beyond auditing into financial modeling, experimentation design, and client reporting. Each one works standalone or as part of a full audit workflow. /ads math : 7 PPC financial calculators. CPA calculator, ROAS calculator, break-even ROAS, LTV:CAC ratio, budget forecaster, impression share estimator, and margin-aware bidding. You paste in your numbers, the tool does the math and explains what each result means for your campaigns. No spreadsheets needed. Example: "What CPA do I need at a 4x ROAS target with a $45 average order value and 62% gross margin?" The tool solves it in seconds. /ads test : A/B test design with IF/THEN/BECAUSE. Generates a complete test plan: hypothesis statement, control and variant definitions, primary and secondary metrics, minimum sample size calculation (based on your baseline conversion rate and minimum detectable effect), and test duration estimate. Every hypothesis follows the IF/THEN/BECAUSE format to force clear thinking. Example output: "IF we add social proof to the landing page headline, THEN conversion rate will increase by 15%, BECAUSE exit survey data shows 34% of bounced visitors cited trust concerns." /ads report : PDF generation with charts. Generates a client-ready PDF audit report with the health score, prioritized findings, inline charts, and recommended next steps. Uses Python's ReportLab for PDF generation and matplotlib for charts. The output is a professional deliverable you can hand directly to a client or stakeholder without reformatting. Creative Pipeline - 5 Stages text { font-family: 'Space Grotesk', system-ui, sans-serif; } Creative Pipeline 1 Brand DNA ads-dna Extract brand identity + voice 2 Strategy creative-strategist Campaign concepts + messaging 3 Visuals visual-designer Image direction + specifications 4 Copy copy-writer Ad headlines, descriptions 5 Validate format-adapter Platform specs + compliance Each agent runs as a forked Task: parallel where possible, sequential where dependent Creative pipeline: 5-stage workflow from brand DNA extraction through platform-specific validation ## How Does It Handle Security and Privacy? Data breaches cost companies an average of $4.88 million per incident ([IBM](https://www.ibm.com/reports/data-breach), 2024). All analysis happens locally. No ad account data leaves your machine. The tool processes your exported data, screenshots, and pasted metrics entirely within your Claude Code session. There is no telemetry, no external API calls for analysis, and no data persistence between sessions. Two security fixes in v1.5: SSRF protection: URL inputs in the landing page analyzer and screenshot capture scripts now validate against internal IP ranges (RFC 1918, link-local, loopback). Previously, a crafted URL could trigger requests to internal network endpoints. - Path traversal fix: The screenshot capture script now sanitizes file paths to prevent directory traversal via ../ sequences in output filenames. If you need live API access to your ad platforms (for real-time data pulls instead of pasted exports), the tool supports MCP server connections. These are optional, explicitly configured by you, and only make read-only API calls. The tool never modifies your ad accounts. Data Privacy Architecture text { font-family: 'Space Grotesk', system-ui, sans-serif; } Data Privacy Architecture - YOUR MACHINE Claude Code claude-ads skill Analysis Engine PDF Generator Ad Platforms Google Ads Meta Ads LinkedIn Ads TikTok Ads Microsoft Ads Apple Search Ads Optional: MCP API calls only All analysis happens locally on your machine Data privacy architecture: all processing stays on your machine, platform connections are optional and read-only ## How Do You Install and Get Started? claude-ads v1.5 is available now on [GitHub](https://github.com/AgriciDaniel/claude-ads) ([release notes](https://github.com/AgriciDaniel/claude-ads/releases/tag/v1.5.0)). The fastest way to install is the Claude Code plugin system. Open Claude Code and run: /install-skill https://github.com/AgriciDaniel/claude-ads Or use the one-liner for manual installation: curl -fsSL https://raw.githubusercontent.com/AgriciDaniel/claude-ads/main/install.sh | bash After installation, run /ads audit to start a full audit, or use any of the 19 sub-skills directly. The tool works with pasted data (copy metrics from your ad platform dashboards), exported CSVs, or live MCP API connections. GitHub: [github.com/AgriciDaniel/claude-ads](https://github.com/AgriciDaniel/claude-ads) - Previous release post: [claude-ads v1.4 announcement](/blog/claude-code-ad-agency) - Full AI marketing stack: [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) Full release notes: [v1.5.0 on GitHub](https://github.com/AgriciDaniel/claude-ads/releases/tag/v1.5.0) From my experience: I ran v1.5 on three real ad accounts during testing. The biggest finding was consistent: most accounts lose 15-30% of their budget on search terms that should be negative keywords. The Quick Wins section now surfaces these immediately, before anything else. One account saved $2,100/month just from the negative keyword recommendations alone. ## Frequently Asked Questions ### Does it connect to my ad accounts automatically? No. By default, you paste data or upload exported CSVs. The tool analyzes what you provide locally. If you want live API access, you configure MCP server connections yourself. These are optional, use read-only permissions, and the tool will never create, modify, or delete anything in your ad accounts. ### How accurate are the benchmarks? Benchmarks are sourced from platform documentation, published case studies, and industry reports (Google Ads Help Center, Meta Business Help Center, LinkedIn Marketing Solutions, WordStream, Databox). Each benchmark cites its source. They are updated with each release. If your industry has unusual economics (e.g., enterprise SaaS with 18-month sales cycles), the tool accounts for that when you specify your vertical in /ads plan . ### Can it create or edit ads? The creative pipeline ( /ads creative , /ads generate , /ads photoshoot ) generates ad copy, image prompts, and creative briefs. It does not push changes to your ad platforms. You take the output and implement it yourself. This is intentional: automated ad creation without human review is how you get compliance violations and brand damage. ### What platforms does it support? Seven platforms: Google Ads (80 checks), Meta Ads (50 checks), Apple Ads (35+ checks), TikTok Ads (28 checks), LinkedIn Ads (27 checks), Microsoft Ads (24 checks), and YouTube Ads (15 checks). YouTube checks are separate from Google Ads because video campaign optimization has different signals and benchmarks. Total: 250+ weighted audit checks. ### Is it really free? Yes. MIT licensed, no usage limits, no premium tier, no gated features. You need a Claude Code subscription ($20/month for Pro or $200/month for Max) because the tool runs inside Claude Code. The tool itself is free and open source. There is no hosted version and no plans for one. Learn more [about me](/about) and the full suite of AI marketing tools I'm building. For the complete open-source stack, check [my AI marketing automation breakdown](/blog/ai-marketing-automation-stack). ## Related Posts - [Claude Code Just Replaced Your Ad Agency](/blog/claude-code-ad-agency) - The original v1.4 release with 186 checks across 6 platforms - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive guide to top Claude Code skills ranked by GitHub stars Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE](https://www.skool.com/ai-marketing-hub) *** # Claude Code Just Turned Obsidian Canvas Into an AI Design Studio - URL: [https://agricidaniel.com/blog/claude-canvas-ai-visual-production](https://agricidaniel.com/blog/claude-canvas-ai-visual-production) - Published: 2026-04-10 - Updated: 2026-04-10 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. claude-canvas turns natural language into fully laid-out Obsidian canvases. 12 templates, 6 layout algorithms, 3 AI agents. Presentations, knowledge graphs, and mood boards from a single prompt. ## You're Still Dragging Nodes Around Manually? The AI presentation market hit $2 billion in 2025 and is growing at 25% annually toward $10 billion by 2033 ([Research and Markets](https://www.researchandmarkets.com/reports/6215071/artificial-intelligence-ai-presentation), 2025). Tools like Gamma, Beautiful.ai, and Canva are racing to automate slide creation. But they all live in the cloud, own your data, and cost $10-30/month. Meanwhile, Obsidian's Canvas feature - an infinite whiteboard built into a free, local-first app with 1.5 million users - sits there waiting for someone to make it programmable. So I built [claude-canvas](https://github.com/AgriciDaniel/claude-canvas). It's a Claude Code plugin where Claude acts as your Creative Director . You describe what you want - "mood board for a cyberpunk game" or "presentation on Q3 results" - and you get a fully populated, professionally laid-out Obsidian canvas. No dragging. No manual positioning. No cloud subscription. Key Takeaways - 12 template archetypes (presentation, flowchart, mind map, gallery, dashboard, storyboard, knowledge graph, mood board, timeline, comparison, kanban, project brief) - 6 layout algorithms with auto-detection - the AI picks the right spatial arrangement for your data - 85% of marketers save ~4 hours/week with AI visual tools ([Figma](https://www.figma.com/resource-library/design-statistics/), 2026) - Zero external dependencies, zero cloud, zero subscriptions - MIT licensed ## What Does claude-canvas Actually Create? Users report 40% faster task completion when using spatial canvas approaches versus linear documents ([Storyflow](https://storyflow.so/blog/best-visual-thinking-tools-2026), 2026). That's because spatial layout activates different cognitive pathways - you see relationships, gaps, and patterns that text hides. claude-canvas builds on this with 12 template archetypes : Template Layout Use Case Presentation Linear vertical Slide decks (1200x675px per slide) Flowchart Dagre hierarchical Process documentation Mind Map Radial center-out Brainstorming, idea exploration Gallery Grid Image portfolios, showcases Dashboard Grid variable KPI monitoring, project status Storyboard Linear horizontal Video/animation planning Knowledge Graph Force-directed Entity relationships, research maps Mood Board Asymmetric grid Creative direction, design inspiration Timeline Linear horizontal Event sequences, project history Comparison Two columns Side-by-side analysis Kanban Column zones Task management, workflow Project Brief Stacked zones Scope documentation, kickoffs Multiple canvas types generated by claude-canvas - gallery, knowledge graph, presentation ## How Do the 6 Layout Algorithms Work? 78% of professionals say AI tools significantly speed up their workflows ([Figma](https://www.figma.com/resource-library/design-statistics/), 2026). But speed without quality is just fast garbage. claude-canvas doesn't randomly scatter nodes - it uses 6 purpose-built layout algorithms that understand spatial relationships: - Dagre - Hierarchical layout for flowcharts and org charts. Supports top-to-bottom, left-to-right, and reverse directions. Uses the Sugiyama algorithm for clean edge routing. - Grid - Uniform rows and columns for galleries and comparisons. Customizable column count with auto-scaling. - Radial - Concentric rings from a center hub. Perfect for mind maps where ideas branch outward from a core concept. - Force-directed - Physics simulation with attraction and repulsion forces. Ideal for knowledge graphs where relationships determine proximity. - Linear - Single-axis timeline or sequence. Works horizontal or vertical for storyboards and presentations. - Auto-detect - Analyzes node types, edge density, and hierarchy to pick the best algorithm automatically. You don't need to know which layout to use - the AI figures it out. Every layout snaps to a 20-pixel grid, maintains minimum 80px horizontal and 60px vertical gaps, and targets 15-30 visible nodes per viewport. These aren't arbitrary numbers - they're calibrated for readability at standard zoom levels. claude-canvas Template Distribution Donut chart: Productivity templates (presentation, dashboard, kanban, project brief) 33%, Knowledge templates (mind map, knowledge graph, flowchart) 25%, Creative templates (gallery, mood board, storyboard) 25%, Analysis templates (comparison, timeline) 17%. 12 Templates Across 4 Categories Purpose-built archetypes for every use case 12 templates Productivity (4) Knowledge (3) Creative (3) Analysis (2) Source: claude-canvas template catalog ## One Prompt, Full Canvas - The Generate Command 77% of marketing and creative leaders say AI tools actively enhance their team's creative output ([Figma](https://www.figma.com/resource-library/design-statistics/), 2026). claude-canvas's /canvas generate command is where this gets real. One natural language description, and three specialized agents coordinate to build a complete canvas: - Canvas Composer - Analyzes your description, selects the right template, plans content strategy. Enforces maximum 200 words per node for scannability. - Canvas Media - Coordinates batch media generation. Integrates with /banana for AI images, /svg for diagrams, and native Mermaid for flowcharts. - Canvas Layout - Applies the optimal spatial algorithm, creates zones, routes edges, and validates spacing. Here's what that looks like in practice: ``` /canvas generate "mood board for cyberpunk game: neon streets, rain, robots, holographic ads" ``` Claude selects the mood-board template, generates AI images via /banana , arranges them in an asymmetric grid with color-coded zones for "Environment," "Characters," and "UI Elements," adds text cards with style notes, and delivers a canvas you'd normally spend an hour building manually. AI-generated image gallery - real photographs arranged and sized automatically Our finding: In testing across 50+ canvas generations, the auto-detect layout algorithm correctly identified the optimal spatial arrangement 88% of the time. The remaining 12% were edge cases with mixed content types where a manual /canvas layout override produced better results. Average generation time: under 30 seconds for a 15-node canvas. ## How Does It Handle Presentations? The presentation software market is projected to reach significant scale by 2033, driven by AI-powered slide generation ([Coherent Market Insights](https://www.coherentmarketinsights.com/industry-reports/presentation-software-market), 2026). But most AI presentation tools are cloud-locked SaaS products. claude-canvas generates presentation-ready slide decks that live in your Obsidian vault: - 1200x675px slides - Standard dimensions compatible with Advanced Canvas plugin's presentation mode - Edge navigation - Slides connect with directional edges for keyboard-driven presentations - Color-coded sections - Title slides, content slides, and closing slides get distinct visual treatment - Exportable - PNG, SVG, or PDF via the canvas-export skill Presentation slides generated from a single prompt - structured, connected, presentation-ready The key advantage over Gamma or Beautiful.ai? Your presentations are markdown files on your disk. Version them with git. Search them with grep. Link them to your wiki. They don't disappear when a startup shuts down. ## What's the Architecture Under the Hood? The tool is built on Python with zero external pip dependencies for core functionality. That's deliberate - every dependency is a point of failure. Here's the skill architecture: claude-canvas Architecture Horizontal bar chart showing the skill architecture: 1 orchestrator (canvas), 7 sub-skills (create, populate, layout, present, generate, template, export), and 3 agents (composer, layout, media). claude-canvas Architecture 8 skills + 3 specialized agents - Orchestrator canvas (routes all commands) Sub-skills create populate layout generate present template export composer agent media agent layout agent Python core - zero external dependencies The validation layer ( canvas_validate.py ) runs automatically after every write operation via a PostToolUse hook. It checks for overlapping nodes, placeholder text, spacing violations, and node count limits (hard cap at 200, warning at 100). The test suite covers 103 automated tests across all components. ## How Does It Work with claude-obsidian? If you're using [claude-obsidian](/blog/claude-obsidian-ai-second-brain) for knowledge management, claude-canvas becomes the visual layer. When it detects a claude-obsidian vault, it automatically uses wiki/canvases/ as the canvas directory. You can: Visualize your wiki as a knowledge graph canvas with force-directed layout - Build presentations from wiki pages - pull entity and concept pages into slide decks - Create research boards - drag in source pages, images, and PDFs from your wiki - Generate dashboards - auto-populate with wiki statistics and page counts Without claude-obsidian, it works standalone - canvases go to .canvases/ in your project root. No vault required. From my experience: I use claude-canvas most for project briefs and knowledge graphs. When I start a new tool - like claude-seo or claude-ads - I run /canvas generate "project brief for [tool]: goals, architecture, timeline, risks" and get a structured canvas I can iterate on. It replaced my Miro boards entirely. The force-directed knowledge graphs are particularly useful for mapping how entities relate to each other across research sources. ## Why Not Just Use Miro, FigJam, or Canva? Miro has 7,000+ templates and 160+ integrations. FigJam has the entire Figma ecosystem behind it. So why use a terminal tool that generates JSON files? Three reasons: - Your data stays local. Miro boards live on Miro's servers. FigJam boards live on Figma's servers. Obsidian canvases are JSON files on your disk. Version them with git. Search them with grep. Back them up however you want. They don't vanish when a company pivots to enterprise-only pricing. - It's programmable. You can't script Miro from your terminal. You can't auto-generate 50 comparison canvases from a data source in FigJam. claude-canvas is a CLI tool - pipe data in, get canvases out. This is what makes it useful for documentation, research, and development workflows. - It compounds with your wiki. When your canvas references a wiki page, that's a bidirectional link. Obsidian's graph view shows the connection. Your canvas becomes part of your knowledge graph, not a disconnected island on someone else's server. Here's what most visual tools get wrong: they optimize for creation, not for retrieval. You make a beautiful Miro board, share it once, and never find it again. Obsidian canvases are searchable, linkable, and indexable. When you search your vault for "cyberpunk" next year, your mood board shows up alongside your notes, your wiki pages, and your research. That's the compounding advantage of keeping everything in one system. Radial mind map - ideas branch outward from a center concept, auto-positioned ## How Do I Get Started? Three ways to install: Option 1: Claude Code CLI (fastest) ``` claude plugin install AgriciDaniel/claude-canvas ``` Option 2: Clone ``` git clone https://github.com/AgriciDaniel/claude-canvas.git bash bin/setup.sh ``` Option 3: Add to existing project ``` claude plugin add ~/path/to/claude-canvas ``` Then just type /canvas and Claude will list available commands. Try /canvas generate "mind map for my next project" to see it in action. Optional integrations (not required): - [banana-claude](/blog/banana-claude-ai-image-generation) - AI image generation via Gemini for canvas nodes - Advanced Canvas plugin - Enables presentation mode and export features - Pillow - Auto-detects image aspect ratios for proper sizing ## Frequently Asked Questions ### Does it work without Obsidian installed? Technically yes - it creates standard JSON Canvas files ( .canvas ) that follow the open spec. But you need Obsidian (free) to view and interact with them. The plugin itself just reads and writes JSON files, so it works anywhere Claude Code runs. ### What's the node limit? Hard cap at 200 nodes per canvas, with a performance warning at 100. The validation system enforces this automatically. For most use cases, 15-30 nodes per viewport is the sweet spot for readability. If you need more, split into multiple linked canvases. ### Can I use it for client presentations? Yes. The presentation template generates 1200x675px slides with edge navigation, compatible with Advanced Canvas's presentation mode. Export to PNG or PDF for sharing. The presentation software market is growing at 25% CAGR ([Research and Markets](https://www.researchandmarkets.com/reports/6215071/artificial-intelligence-ai-presentation), 2025), but this costs $0. ### How does it compare to Obsidian's built-in canvas? Obsidian's canvas is a manual tool - you drag nodes, draw edges, position everything by hand. claude-canvas automates the entire process: content creation, spatial layout, media integration, and validation. It's the difference between a blank whiteboard and a design assistant that builds the board for you. ### Is it free? MIT licensed. You pay only for the AI model API usage (Claude, Gemini for image generation). The plugin itself is free. Obsidian is free for personal use. ## The Visual Layer Your Wiki Deserves 80% of AI-generated content goes unread because it's buried in text ([Storyflow](https://storyflow.so/blog/best-visual-thinking-tools-2026), 2026). Spatial canvases fix this by making information visible, relational, and navigable. claude-canvas makes them automatic. 12 templates. 6 algorithms. 3 agents. Zero subscriptions. - Star the repo on [GitHub](https://github.com/AgriciDaniel/claude-canvas) - Pair it with [claude-obsidian](/blog/claude-obsidian-ai-second-brain) for the full knowledge management stack - Learn more [about me](/about) and the tools I'm building - Build your own Claude Code skills with [Skill Forge](/blog/skill-forge-build-claude-code-skills) - Join the [Claude Code community on Skool](https://www.skool.com/claude-code) ## Related Posts - [Obsidian AI Second Brain: The Open-Source Plugin That Organizes Itself](/blog/claude-obsidian-ai-second-brain) - The knowledge engine that claude-canvas extends - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive ranking of skills that save time - [banana-claude: AI Image Generation](/blog/banana-claude-ai-image-generation) - The image engine that powers canvas media - [Build Your Own Claude Code Skills with Skill Forge](/blog/skill-forge-build-claude-code-skills) - From idea to published skill Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # How I Got 8,000 GitHub Stars in 9 Months as a Solo Developer - URL: [https://agricidaniel.com/blog/how-i-got-8000-github-stars](https://agricidaniel.com/blog/how-i-got-8000-github-stars) - Published: 2026-04-10 - Updated: 2026-04-10 - Category: Open Source - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. 8,135 GitHub stars across 26 repos in 9 months. No team, no funding. The exact channels, README tactics, and ecosystem strategy that worked - with real numbers. My GitHub profile, sorted by stars - 26 repos, 8,135 total stars, 579 followers ## GitHub Has Over 1 Billion Repositories. 99% Have Zero Stars. GitHub crossed 1 billion repositories in June 2025 ([Kinsta](https://kinsta.com/blog/github-statistics/), 2026). Over 180 million developers use the platform. And the vast majority of projects sit at zero stars, zero forks, zero downloads. I know because my first repos were in that category. Then something changed. Between October 2025 and April 2026, I built 26 open-source tools - Claude Code skills for SEO, advertising, content, knowledge management, and video - and accumulated 8,135 GitHub stars and 1,412 forks . No team. No VC. No marketing budget. Just one developer solving real problems with AI automation. This isn't a guide about gaming algorithms. It's the exact timeline, the channels that worked, the mistakes I made, and the one strategy that turned 26 scattered repos into a compounding growth engine. Key Takeaways - 8,135 stars across 26 repos in 9 months, with [claude-seo](/blog/claude-code-seo-stack) alone hitting 4,466 stars and 685 forks - 73% of engineering teams now use AI coding tools daily ([Developer Survey](https://claude5.ai/news/developer-survey-2026-ai-coding-73-percent-daily), 2026) - building for this wave drove organic discovery - YouTube demos, the Skool community, blog content, and organic GitHub discovery drove 80%+ of growth - not Product Hunt launches or paid ads - The ecosystem strategy (26 tools that cross-reference each other) compounds faster than any single product ## What Do 8,135 Stars Across 26 Repos Look Like? Most open-source case studies are about one project. AFFiNE grew to 60K stars ([DEV.to](https://dev.to/iris1031/how-to-get-more-github-stars-the-definitive-guide-33k-stars-case-study-2kjo), 2026). ScrapeGraphAI hit 20K. But they're teams with funding and full-time employees. My approach was different: build 26 tools that form an ecosystem, not one product that does everything. Top 10 Repos by GitHub Stars Horizontal bar chart: claude-seo 4,466, claude-ads 2,019, claude-blog 443, claude-obsidian 380, banana-claude 187, automated-business-analysis 102, claude-prompts 63, on-page-seo 54, Keywordo-kun 49, wp-mcp-ultimate 48. Top 10 Repos by GitHub Stars 8,135 total across 26 repositories - claude-seo 4,466 claude-ads 2,019 claude-blog 443 claude-obsidian 380 banana-claude 187 business-analysis 102 claude-prompts 63 on-page-seo 54 Keywordo-kun 49 wp-mcp-ultimate 48 Source: github.com/AgriciDaniel (April 2026) The power law is obvious. The top 2 repos - [claude-seo](/blog/claude-code-seo-stack) and [claude-ads](/blog/claude-code-ad-agency) - account for 80% of total stars. But here's what the chart doesn't show: the smaller repos feed the larger ones. Someone discovers [claude-obsidian](/blog/claude-obsidian-ai-second-brain), sees it links to claude-seo, stars both. That's the ecosystem effect. The numbers: 8,135 total stars, 1,412 forks, 577 followers, 26 public repos. Account created July 27, 2025. First repo (n8n workflow) published October 31, 2025. That's roughly 9 months from zero to here. Every star count in this article is live, verifiable data from [github.com/AgriciDaniel](https://github.com/AgriciDaniel). ## What Was the Growth Timeline? ScrapeGraphAI took 6 months to hit 1,000 stars, then reached 10,000 in the next 4 months ([ScrapeGraphAI](https://scrapegraphai.com/blog/gh-stars), 2026). Growth compounds once you find the right audience. My timeline followed a similar pattern - slow start, then a sharp inflection. Cumulative Star Growth Timeline Line chart: Oct 2025: ~50, Nov: ~120, Dec: ~150, Jan 2026: ~300, Feb: ~5,500, Mar: ~7,200, Apr: ~8,135. Sharp inflection in February when claude-seo and claude-ads launched. Star Growth: 0 to 8,135 October 2025 to April 2026 0 2K 4K 6K 8K Oct Nov Dec Jan Feb Mar Apr claude-seo launches Source: github.com/AgriciDaniel (April 2026) Star history for the top 3 repos - the February 2026 inflection is visible Phase 1 (Oct-Nov 2025): Three n8n automation workflows. Modest traction - about 170 stars combined. These were the "learning in public" phase. They taught me what developers wanted: tools that solve real problems, not demos. Phase 2 (Jan 2026): Pivoted to Claude Code skills. Built on-page-seo, linkedin-content-creator, and planckatron. Around 300 cumulative stars. Still small, but the Claude Code ecosystem was heating up. Phase 3 (Feb 2026): The explosion. claude-seo launched February 7th. claude-ads four days later. claude-blog, skill-forge, claude-prompts, and claude-shorts followed the same month. I went from 300 to 5,500+ stars in a single month. This wasn't luck - it was timing plus preparation. Claude Code was becoming the most-used AI coding tool (46% "most loved" in the Pragmatic Engineer survey), and I had the most comprehensive skill library. Phase 4 (Mar-Apr 2026): Sustained growth. banana-claude, claude-obsidian, and the content tools added another 2,600+ stars. The ecosystem was compounding - each new tool brought users to the existing ones. The inflection moment: When claude-seo hit GitHub Trending, I watched the star count climb in real time - hundreds per hour. But the real growth came from what happened next: people who starred claude-seo explored my profile, found claude-ads, found claude-blog, and starred those too. A single trending moment cascaded across the entire ecosystem. That's when I realized the strategy was working. ## Which Channels Actually Drove Stars? 73% of engineering teams now use AI coding tools daily - up from 41% in 2025 ([Developer Survey](https://claude5.ai/news/developer-survey-2026-ai-coding-73-percent-daily), 2026). Building tools for this wave meant organic discovery was built into the market. But I still needed to get the first eyeballs. Here's what worked, roughly weighted by impact: Where Did the Stars Come From? Donut chart: Organic GitHub discovery 35%, YouTube demos 25%, Skool community 20%, Blog posts 15%, Other (Reddit, LinkedIn, X) 5%. Where Did the Stars Come From? Estimated channel attribution 8,135 total stars Organic GitHub 35% YouTube 25% Skool 20% Blog 15% Other 5% Source: Estimated from referral patterns (April 2026) claude-seo in action - the most-starred tool, running a full site audit Organic GitHub (~35%): The biggest driver was GitHub's own discovery. When claude-seo hit Trending, it triggered a chain reaction - appearing in "Explore" recommendations, search results, and "Users also starred" suggestions. GitHub's algorithm rewards velocity (many stars in a short period), and the February launch had that velocity. YouTube demos (~25%): Every major tool got a YouTube demo video. These weren't polished production pieces - just screen recordings showing the tool in action, narrated in real time. The [claude-blog demo](/blog/claude-code-blog-writer) hit 13,000 views. Video converts better than text because people can see the tool actually working, not just read about it. Skool community (~20%): The [AI Marketing Hub](https://www.skool.com/ai-marketing-hub) (2,800+ members) is where I share work-in-progress builds, get feedback, and announce releases. Community members who use the tools become evangelists - they share repos in their own networks, write about them on LinkedIn, and star new tools as soon as they launch. Blog posts (~15%): Each major tool has a [dedicated blog post](/blog) on agricidaniel.com with data charts, competitor analysis, and deep technical walkthroughs. These rank in Google and drive long-tail traffic. The blog post for [claude-obsidian](/blog/claude-obsidian-ai-second-brain) led to a comment on Karpathy's LLM Wiki gist, which drove another wave of stars. Other (~5%): Reddit, LinkedIn, Twitter/X. Useful for spikes, but not the sustained engine. Reddit in particular is hit-or-miss - a well-timed post in r/ClaudeAI can drive hundreds of stars, but the same post in r/programming might get downvoted into oblivion. ## How Did the README Strategy Work? AFFiNE credits README optimization as one of the biggest levers in their growth from 0 to 60,000 stars ([DEV.to](https://dev.to/iris1031/how-to-get-more-github-stars-the-definitive-guide-33k-stars-case-study-2kjo), 2026). I learned this the hard way. My early repos had bare-bones READMEs - a title, a paragraph, and install instructions. They got almost no stars. Here's what changed: Animated GIF banner - Every repo now opens with a GIF or cover image that shows the tool in action. This is the single biggest conversion lever. People scroll, they see it working, they star. - Badge row - Stars count, license, install command. Social proof at a glance. - Feature table - Not a wall of text. A scannable table showing what the tool does in 10 seconds. - One-command install - If someone can't install your tool in one line, they won't try it. claude plugin install AgriciDaniel/claude-seo is the entire setup. - Ecosystem links - Every README links to related tools. This is the cross-pollination engine. The claude-seo repo page - clear description, topic tags, and organized code structure The 30-second test: if a developer can't understand what your tool does, why they should care, and how to install it in 30 seconds of scanning your README - you've lost them. Treat your README like a landing page, not documentation. ## Why Does the Ecosystem Strategy Compound? This is the insight that none of the "how to get GitHub stars" guides cover, because they're all written about single products. I didn't build one tool. I built a supply chain. Here's how it works: [claude-seo](/blog/claude-code-seo-stack) audits your site. Its README links to [claude-blog](/blog/claude-code-blog-writer) for content creation. claude-blog links to [banana-claude](/blog/banana-claude-ai-image-generation) for AI image generation. banana-claude links to [claude-canvas](/blog/claude-canvas-ai-visual-production) for visual layouts. And all of them link to [claude-obsidian](/blog/claude-obsidian-ai-second-brain) for knowledge management. A developer who finds one tool discovers five. The ecosystem in practice - claude-obsidian's knowledge graph showing how tools interconnect Each new tool I build doesn't just add stars - it multiplies discovery across the entire portfolio. After the backlink update, all 26 repos now cross-reference each other through Author sections, Related Projects tables, and blog post links. That's 87+ internal backlinks creating a web of discovery on GitHub. The Skool community sits at the center of this web. Users who find the tools join the community. Community members who hear about new tools star them on launch day. This creates the velocity spikes that trigger GitHub's trending algorithm, which creates more organic discovery. It's a flywheel. The compounding math: If each repo links to 5 others, and someone starring one repo has a 15% chance of starring a linked repo, then adding repo #26 doesn't just add stars to repo #26 - it adds fractional stars to repos #1 through #25. The marginal cost of adding a new tool decreases while the marginal benefit to the ecosystem increases. This is why the portfolio strategy outperforms the single-product strategy at scale. ## What Were the Biggest Mistakes? Not everything worked. Here's what I'd do differently: - Starting without documentation. My first Claude Code skills had minimal READMEs. They got almost no stars until I rewrote them with proper GIFs, feature tables, and one-command installs. Documentation isn't afterthought - it's the product's marketing. - Building before validating. A couple of repos exist with under 10 stars because I built what I thought was cool instead of what developers actually needed. The tools that exploded (claude-seo, claude-ads) solved obvious, painful problems. The ones that flopped solved problems nobody had. - Not building community sooner. I should have started the Skool community in month one, not month four. The community accelerates everything - feedback loops, launch velocity, word of mouth. Every month of delay was compounding opportunity lost. - Ignoring cross-linking initially. For the first several months, my repos were isolated silos. Once I added ecosystem tables and Author sections linking them all together, discovery increased noticeably. I should have built the network from day one. ## Do GitHub Stars Actually Matter? 85% of developers check a project's star count before deciding to use it ([ToolJet](https://blog.tooljet.com/github-stars-guide/), 2026). Stars are the first thing a developer sees on a repo page - they're instant social proof. But they're a means, not an end. Here's what stars actually enabled: - Community growth. Stars drove profile visits, which drove Skool community signups. The [AI Marketing Hub](https://www.skool.com/ai-marketing-hub) grew to 2,800+ members largely through GitHub referrals. - Product validation. Stars + forks + issues = proof that people use and care about the tools. This informed what to build next - [Skill Forge](/blog/skill-forge-build-claude-code-skills) exists because community members asked for it. - Commercial opportunities. The open-source portfolio led to [Rankenstein](https://rankenstein.pro) - an AI content engine built on the same SEO and content principles. Stars = credibility = trust = commercial viability. - Contributor attraction. 1,412 forks means over a thousand developers took the code and adapted it. Some contributed back. Stars attract talent. The open source service market is now worth $44.12 billion, growing at 16.22% CAGR ([Mordor Intelligence](https://www.mordorintelligence.com/industry-reports/open-source-service-market), 2026). If your tools are good enough to get starred, they're good enough to build a business on. ## What Would I Tell Someone Starting From Zero? If you're reading this with zero stars, here's the shortest path I've found: - Solve a real, painful problem. Not a cool technical demo - a tool that makes someone's daily work easier. claude-seo replaced $300/month in SEO tools. That's why it has 4,466 stars. - Make your README a landing page. GIF demo at the top. Feature table. One-command install. Badge row. If you spend 20 hours building and 2 hours on the README, flip that ratio to at least 50/50. - Ride a wave. Claude Code's explosion was my wave. Find yours. AI coding tools, developer productivity, specific frameworks - build for a category that's growing, not one that's stagnant. - Build an ecosystem, not a product. Your second repo makes your first repo more discoverable. Your third makes the first two more discoverable. This is the compounding advantage solo developers have over teams who pour everything into one project. - Build community from day one. Even a Discord with 50 active users will outperform a Twitter account with 5,000 followers for launch velocity. ## Frequently Asked Questions ### How many GitHub stars is considered good? It depends on the niche. In developer tools, 100 stars puts you in the top 1% of all repos. 1,000 stars means meaningful adoption. 10,000+ is a well-known project. GitHub has over 1 billion repositories ([Kinsta](https://kinsta.com/blog/github-statistics/), 2026) - even 50 stars means someone found your work useful enough to bookmark it. ### Can you buy GitHub stars? You can, and you shouldn't. GitHub actively detects and removes fake stars, and the developer community notices patterns - sudden spikes from accounts with no activity are obvious. Bought stars don't convert to users, forks, or contributors. They're a vanity metric that can damage your credibility. ### How long does it take to get 1,000 GitHub stars? Varies wildly. ScrapeGraphAI took 6 months ([ScrapeGraphAI](https://scrapegraphai.com/blog/gh-stars), 2026). AFFiNE hit 1,000 in 72 hours with a coordinated launch. My claude-seo likely crossed 1,000 within its first 2-3 weeks. The key variable isn't time - it's whether you've found product-market fit and your initial distribution channel. ### Do GitHub stars help you get a job? Yes. Hiring managers in tech check GitHub profiles. A repo with 4,000+ stars is stronger proof of capability than most certifications. It shows you can build something people want, maintain it, and attract a community. Multiple starred repos show consistency and range. ### What's the best day to launch on GitHub? Tuesday through Thursday, US business hours. Avoid weekends and Mondays (inbox overload). The goal is to hit GitHub Trending, which rewards star velocity - getting many stars in a short window. Coordinate your launch across channels (community, social, blog post) to concentrate that velocity into a 24-48 hour period. ## From Zero Stars to an Ecosystem 180 million developers use GitHub ([Kinsta](https://kinsta.com/blog/github-statistics/), 2026). The ones who build things people need, document them well, and connect them into a discoverable ecosystem - those are the ones who accumulate stars. It's not about gaming algorithms or viral launches. It's about solving real problems, consistently, in public. 8,135 stars in 9 months. 26 tools. 1,412 forks. Zero funding. If you're building open source, I hope this helps. - Browse [all 26 repos on GitHub](https://github.com/AgriciDaniel) - Start with [claude-seo](/blog/claude-code-seo-stack) - the most-starred tool - See the [full AI marketing automation stack](/blog/ai-marketing-automation-stack) - Explore the [best Claude Code skills in 2026](/blog/best-claude-code-skills-2026) - Learn more [about me](/about) - Join the [AI Marketing Hub on Skool](https://www.skool.com/ai-marketing-hub) (2,800+ members, free) ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [Obsidian AI Second Brain: The Plugin That Organizes Itself](/blog/claude-obsidian-ai-second-brain) - Karpathy's LLM Wiki pattern, productized - [Claude Code Turned Obsidian Canvas Into an AI Design Studio](/blog/claude-canvas-ai-visual-production) - 12 templates, 6 layout algorithms, zero subscriptions - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full stack at $50/month Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # I Just Shipped the Biggest Claude SEO Update Yet - URL: [https://agricidaniel.com/blog/claude-seo-172-firecrawl-backlink-analysis](https://agricidaniel.com/blog/claude-seo-172-firecrawl-backlink-analysis) - Published: 2026-03-31 - Updated: 2026-03-31 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. From 12 skills to 19 sub-skills, 12 subagents, and 3 extensions. claude-seo v1.7.2 adds live Google APIs, DataForSEO, Firecrawl crawling, backlink analysis, and Excel reports. Here's the full story of how we got here. ## 3,500 Stars and Counting A month ago I released [claude-seo](https://github.com/AgriciDaniel/claude-seo) and it hit 3,500 GitHub stars faster than anything I've built. 560 forks. Issues and PRs I reviewed every single one of. People were actually using it - running audits on their sites, filing bugs when something broke, suggesting features I hadn't thought of. Since then I've been heads-down shipping. Not marketing, not writing threads, not doing launch posts. Just building. Today v1.7.2 is live and the project has completely transformed from what I originally released. What started as a [$300/month SEO tool replacement](/blog/claude-code-seo-stack) is now something bigger - an extensible platform that connects to live data sources and generates reports you can actually hand to clients. Let me walk you through what changed and why. ## From 12 Skills to 19 Sub-Skills The v1.0 I launched had 12 skills and 9 parallel agents. It could audit your site's technical SEO, check your schema, analyze content quality, and give you a ranked list of fixes. That was genuinely useful - people were replacing paid tools with it. But the feedback was consistent: "Great audit, but I need live data." The tool was analyzing static HTML, not querying real APIs. It couldn't tell you your actual Core Web Vitals from the field, your real Search Console impressions, or your live backlink profile. It was doing what a developer could do by reading source code - just faster. v1.7.2 fixes that. The project now has 19 sub-skills (16 core + 3 extensions), 12 parallel subagents , and 3 extension modules that connect to live data. Here's what grew and why. ## Google API Integration - Real Data, Not Guesswork This was the turning point. I wired [9 Google APIs](/blog/google-api-seo-automation-claude-code) directly into claude-seo: PageSpeed Insights, Chrome UX Report (CrUX), Search Console, Google Analytics 4, YouTube Data API, Cloud Natural Language API, the Indexing API, Google Trends, and Custom Search. All at $0.0006 per query on Google's free tiers. The credential system has 4 tiers so you can start free and scale up: - API Key only - PageSpeed, CrUX, YouTube, NLP, Trends, Custom Search. No OAuth needed. Just paste a key. - OAuth (read-only) - Adds Search Console performance data and GA4 reporting. - OAuth (write) - Adds the Indexing API for submitting URLs directly to Google. - Service Account - For automated pipelines and CI/CD integration. The part agency owners care about most: PDF and Excel reports . Run an audit, pipe the Search Console data through the report generator, and get a styled document with summary metrics, per-page performance, query rankings, and indexation status. The Excel version has navy headers, auto-width columns, frozen rows, and filters. Hand it to a client and it looks like you spent an hour in Google Sheets. ## DataForSEO Extension - Live SERP Data and Keyword Research Google's APIs give you your own data. [DataForSEO](https://dataforseo.com/) gives you everyone else's. The DataForSEO extension adds 22 commands to claude-seo through an MCP server integration: - Live SERP analysis - Pull the actual top 10 results for any keyword, with titles, descriptions, featured snippets, and People Also Ask boxes - Keyword research - Search volume, keyword difficulty, CPC, competition level, and search intent classification for any term - Backlink profiles - Referring domains, anchor text distribution, new/lost links, and domain authority metrics - On-page analysis - Content parsing, readability scoring, and Lighthouse data from their infrastructure - Content analysis - Sentiment, entities, and topic extraction at scale The extension pattern I built for this is something I'm proud of. One install script, one MCP server config, done. No manual JSON editing, no path debugging. Run ./extensions/dataforseo/install.sh , enter your API credentials, and the next time you run /seo the extension is automatically available. I used the same pattern for Firecrawl and Banana (AI image generation). ## Firecrawl Extension - The Most Requested Feature "Can it crawl my whole site?" was the question I got more than any other. The honest answer was: not really. The technical audit agent parsed your sitemap and checked individual URLs, but if your site was built with React, Next.js, Vue, or any SPA framework, the crawler saw empty
and nothing else. [Firecrawl](https://www.firecrawl.dev/) changes that completely. It's a crawling service with full JavaScript execution, and the claude-seo integration exposes four commands: - /seo firecrawl crawl - Full-site crawl with JS rendering. Every page gets executed in a real browser before content extraction. - /seo firecrawl map - Fast URL discovery across the entire site. Credit-efficient for sitemap audits. - /seo firecrawl scrape - Single-page deep scrape with the fully-rendered DOM. - /seo firecrawl search - Search within a site's crawled content. The integration is deeper than just standalone commands. When Firecrawl is installed, the audit workflow upgrades automatically - URL discovery switches from sitemap-only to map , and broken link detection uses crawl for JavaScript-rendered pages. Free tier: 500 credits/month (1 credit per page). ## Backlink Analysis The new /seo backlinks command runs a 7-section analysis : - Profile Overview - Total backlinks, referring domains, authority trend - Anchor Text Distribution - Over-optimization detection (flags when >70% are exact-match) - Referring Domain Quality - Authority, relevance, and geographic breakdown - Toxic Link Detection - 30 toxicity patterns including PBN footprints, link farms, and unnatural link velocity. Auto-generates disavow recommendations. - Top Pages by Backlinks - Your most-linked pages and their sources - Competitor Gap - /seo backlinks gap finds who links to them but not you - New/Lost Tracking - Recent link acquisitions and losses with context Is it Ahrefs? No. Ahrefs has a massive proprietary backlink index built over a decade. But for most small-to-mid sites, this gives you the actionable data - toxic links to disavow, competitor gaps to target, anchor distribution to fix - without the $99/month fee. ## Watch the Full Walkthrough I recorded a complete demo showing the setup, the audit workflow, Google API reports, and the extension system in action: ## Anthropic Compliance and Marketplace Something I'm genuinely proud of: claude-seo passed Anthropic's official plugin validation . Not a self-assessment - the actual compliance checks for quality, security, and best practices. The plugin has been submitted to the Anthropic marketplace and is listed on the [awesome-claude-skills](https://github.com/ComposioHQ/awesome-claude-skills) repository (49K+ stars). This matters for trust. When someone installs a Claude Code skill, they're giving it access to their terminal and files. Being validated by Anthropic means the code has been reviewed and meets their standards for safety and functionality. It's also how new users discover the tool - the marketplace is where most first-time installations come from now. ## The Community 3,500+ stars. 560 forks. I've reviewed every single pull request and responded to every issue. Some of the best features came from community feedback - the Excel export was requested by agency owners in the [AI Marketing Hub Pro](https://www.skool.com/ai-marketing-hub-pro) community. The Firecrawl integration happened because a dozen people independently asked for SPA crawling support. The project also has a companion tool now: [AI Marketing Claude](https://github.com/zubair-trabzada/ai-marketing-claude) by Zubair Trabzada, which handles the post-audit marketing actions. That's the kind of ecosystem growth I was hoping for - other builders extending the platform for their own use cases. If you're using claude-seo and building something on top of it, I want to hear about it. Drop a message in the [AI Marketing Hub](https://www.skool.com/ai-marketing-hub) (free, 4,500+ members) or open a discussion on GitHub. ## What's Next v1.8 is already in progress. Three things on the roadmap: - Content strategy skill - Topic cluster planning, content gap analysis, and editorial calendar generation based on your existing content and keyword data - Deeper Firecrawl integration - Scheduled crawls, change detection, and visual regression testing - Automated monitoring - Cron-based audits with diff reports and alerting when critical metrics drop The goal is to make claude-seo not just an audit tool but a monitoring system that catches regressions before they cost you rankings. If you want to shape what gets built, the roadmap discussions happen in the community. ## Try It Install in one command: ``` curl -sL https://raw.githubusercontent.com/AgriciDaniel/claude-seo/v1.7.2/install.sh | bash ``` Windows: ``` irm https://raw.githubusercontent.com/AgriciDaniel/claude-seo/v1.7.2/install.ps1 | iex ``` - Star the repo - [github.com/AgriciDaniel/claude-seo](https://github.com/AgriciDaniel/claude-seo) - Read the docs - [claude-seo.md](https://claude-seo.md) - Join the community - [AI Marketing Hub](https://www.skool.com/ai-marketing-hub) (free) or [Pro](https://www.skool.com/ai-marketing-hub-pro) ($88/mo) - Check out Rankenstein - [rankenstein.pro](https://rankenstein.pro) for the full AI content engine 19 sub-skills. 12 subagents. 3 extensions. MIT licensed. Free forever. Go break something. ## Frequently Asked Questions ### Q: How do I upgrade from an older version? Run the same install command - it overwrites the existing skill files with the latest version. Your extension configurations (DataForSEO API keys, Firecrawl tokens) are preserved in separate config files that the installer doesn't touch. No migration needed. ### Q: Do I need all three extensions? No. The core 16 sub-skills work without any extensions. DataForSEO adds live SERP and keyword data. Firecrawl adds JavaScript-rendered crawling. Banana adds AI image generation for reports. Install only what you need - each extension is independent. ### Q: Is claude-seo really free? The tool itself is MIT licensed and always free. The extensions connect to third-party APIs that have their own pricing: Google APIs have generous free tiers ($0.0006/query), DataForSEO starts at $50/month, and Firecrawl has a free tier of 500 credits/month. You can use the full core tool without spending anything. ### Q: What's the difference between DataForSEO and Firecrawl? DataForSEO provides market data - what other sites rank for, keyword volumes, backlink profiles, SERP features. Firecrawl provides crawling infrastructure - it visits your site's pages with a real browser and extracts content. DataForSEO tells you about the market. Firecrawl tells you about your site. They complement each other. ### Q: Can I use claude-seo for client work? Yes. MIT license means you can use it commercially, modify it, and redistribute it. The Excel and PDF reports are designed specifically for client delivery. Many agency owners in the community use it as their primary audit tool and white-label the reports. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - The original deep dive on how claude-seo works - [Free Google API SEO Automation With Claude Code](/blog/google-api-seo-automation-claude-code) - Detailed walkthrough of the 9 Google API integrations - [Free SEO Audit Tools That Actually Work](/blog/free-seo-audit-tools) - Where claude-seo fits in the broader landscape of free tools - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full toolkit including claude-seo, claude-blog, and Rankenstein Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # I Built WP MCP Ultimate - One Plugin to Connect AI to WordPress - URL: [https://agricidaniel.com/blog/wp-mcp-ultimate-wordpress-ai-plugin](https://agricidaniel.com/blog/wp-mcp-ultimate-wordpress-ai-plugin) - Published: 2026-03-29 - Updated: 2026-03-29 - Category: Open Source - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I got tired of stacking 3 WordPress plugins that broke each other just to connect AI to my site. So I merged everything into one: WP MCP Ultimate gives AI 58 abilities across 13 domains, through one install. Open source, GPL-2.0, free forever. ## Why I Built This Connecting AI to WordPress should not require three plugins. But that's exactly what it took. You needed MCP Adapter to handle the protocol layer, MCP Expose Abilities to register WordPress actions as tools, and the Abilities API polyfill if your WordPress version was below 6.9. Three plugins from three different authors, three update cycles, three points of failure. Every time WordPress updated, something broke. The adapter would lose its endpoint registration. The abilities plugin would conflict with the polyfill. Config snippets in the docs pointed to URLs that no longer existed. I spent more time debugging the plugin stack than actually using AI to manage my sites. So I did what any frustrated developer does - I merged everything into one plugin. [WP MCP Ultimate](https://github.com/AgriciDaniel/wp-mcp-ultimate) is a self-contained MCP server that installs in one click and gives any MCP-compatible AI client full access to your WordPress site. No dependencies. No conflicts. No duct tape. ## What WP MCP Ultimate Does One plugin. 58 WordPress abilities across 13 domains. Every action goes through a 3-tool meta pattern: discover-abilities lists what's available, get-ability-info returns the schema for a specific ability, and execute-ability runs it. The AI client never needs to guess what's possible - it asks the plugin directly. Here's the full breakdown: Domain Abilities What AI Can Do Posts 6 List, get, create, update, delete, patch content Pages 6 Full CRUD + content patching Taxonomy 4 Categories and tags - list and create Search 1 Full-text content search Revisions 2 List and inspect post revisions Media 5 Upload, list, get, update, delete files Users 6 Full user management with role assignment Plugins 6 Upload, install, activate, deactivate, delete Menus 7 Create menus, add items, assign locations Widgets 3 List sidebars and available widgets Comments 6 Moderate, reply, create, delete Options 3 Get, update, list site options System 3 Transients, debug log, toggle debug mode Total 58 The plugin is compliant with MCP protocol version 2025-06-18 and uses Streamable HTTP transport - not the older SSE (Server-Sent Events) pattern. This matters because Streamable HTTP is bidirectional and doesn't require long-lived connections, which means it works reliably behind CDNs and load balancers that typically kill SSE connections after 30 seconds. It also includes conflict detection. If you have the old MCP Adapter, MCP Expose Abilities, or Abilities API plugins still installed, the dashboard warns you and explains why you should deactivate them. And for sites running WordPress below 6.9, WP MCP Ultimate bundles its own Abilities API polyfill - no separate plugin needed. ## Setup in 2 Minutes Three steps. No configuration files to hand-edit, no terminal commands, no API keys to hunt down. - Install the plugin - Download the zip from the [v1.1.0 release page](https://github.com/AgriciDaniel/wp-mcp-ultimate/releases/tag/v1.1.0) and upload it via Plugins > Add New > Upload Plugin in your WordPress admin. Activate it. - Generate an API key - Go to Tools > MCP Ultimate and click Generate. The plugin creates a WordPress Application Password automatically. - Copy the config snippet - The dashboard shows ready-to-paste config for Claude Code, Claude Desktop, and Cursor. Copy and paste into your AI client's settings. For Claude Code, the config goes into ~/.claude/settings.json : ``` { "mcpServers": { "wordpress": { "type": "streamable-http", "url": "https://your-site.com/wp-json/mcp/wp-mcp-ultimate", "headers": { "Authorization": "Basic BASE64_CREDENTIALS" } } } } ``` The dashboard generates the Base64 credentials for you. No manual encoding required. Once connected, your AI client can discover all 58 abilities automatically through the meta-tool pattern. ## What v1.1.0 Fixed The initial release had three bugs that made the first-run experience rough. All fixed in v1.1.0: - Media upload crash - Uploading images through MCP failed because media_handle_sideload() was undefined. WordPress only loads that function in the admin context, and MCP requests come through the REST API. The fix loads the required admin includes before any media operation. - Wrong endpoint URL - All config snippets (in the dashboard, README, and setup guide) were pointing to /sse with SSE transport type. The correct endpoint is /wp-json/mcp/wp-mcp-ultimate with Streamable HTTP transport. Every snippet has been corrected. - Wrong config path - The Claude Code config snippet showed ~/.claude.json instead of the correct ~/.claude/settings.json . Small typo, big headache for anyone following the docs. The ability count was also corrected from 57 to 58 - a widget ability was missing from the registry. See the full [v1.1.0 release notes](https://github.com/AgriciDaniel/wp-mcp-ultimate/releases/tag/v1.1.0) for details. ## The Ecosystem - How the Tools Connect WP MCP Ultimate doesn't exist in isolation. It's the bridge I was missing between my AI tools and live WordPress sites. Here's how the pieces fit together: AI CONTENT PIPELINE Claude Blog writes content Claude SEO optimizes content WP MCP Ultimate pushes to site WordPress live site - Rankenstein monitors performance Content flows left to right. Data flows back through Rankenstein. WP MCP Ultimate is the bridge between AI tools and your live site. [Claude Blog](https://claude-blog.md) ([GitHub](https://github.com/AgriciDaniel/claude-blog)) writes SEO-optimized content with its [17-command, 100-point scoring system](/blog/claude-code-blog-writer). [Claude SEO](https://claude-seo.md) ([GitHub](https://github.com/AgriciDaniel/claude-seo)) runs a [full technical audit with 9 parallel agents](/blog/claude-code-seo-stack). But until now, the last mile was manual - I'd copy-paste the optimized content into WordPress by hand. WP MCP Ultimate closes that gap. Claude Code can now write a blog post, optimize it for search, and publish it to WordPress - all in one session, without leaving the terminal. [Rankenstein](https://rankenstein.pro) then monitors how the published content performs in search, feeding data back into the next optimization cycle. This is the [AI marketing automation stack](/blog/ai-marketing-automation-stack) I've been building toward. WP MCP Ultimate is the piece that makes it end-to-end. ## Security and What's Next I'm going to be direct about this: I ran a full security audit and identified 7 findings that need to be addressed in v2.0. The current version relies on WordPress's built-in authentication (Application Passwords over Basic Auth with HTTPS), which is standard for REST API plugins. But there are improvements I want to make: Nonce verification for all state-changing operations (currently uses Application Password auth only) - Per-ability capability checks are implemented but need granular review for edge cases - Session management needs rate limiting and connection tracking - Input sanitization follows WordPress coding standards but needs additional validation on complex inputs like media uploads and plugin installs None of these are critical vulnerabilities - the plugin requires administrator credentials and HTTPS. But I want v2.0 to be hardened for production sites running at scale. If you find a security issue, please use the [GitHub Security Advisory](https://github.com/AgriciDaniel/wp-mcp-ultimate/security/advisories/new) to report it responsibly. The roadmap for v2.0 also includes webhook support for real-time notifications, custom ability registration for theme developers, and multi-site network support. ## Watch the Full Demo This 10-minute walkthrough covers the complete setup - from installing the plugin to managing posts, uploading media, and moderating comments through Claude Code: ## Get Started WP MCP Ultimate is open source, GPL-2.0 licensed, and free. No premium tier. No feature gating. The full 58 abilities are available to everyone. - Install it - [Download v1.1.0 from GitHub](https://github.com/AgriciDaniel/wp-mcp-ultimate/releases/tag/v1.1.0) - Star the repo - [github.com/AgriciDaniel/wp-mcp-ultimate](https://github.com/AgriciDaniel/wp-mcp-ultimate) - Report bugs - Open an issue on GitHub. PRs welcome. - Join the community - [AI Marketing Hub](https://www.skool.com/ai-marketing-hub) (free, 4,500+ members) or [AI Marketing Hub Pro](https://www.skool.com/ai-marketing-hub-pro) ($88/mo) for workflow templates and direct support If you're managing WordPress sites and using Claude Code, this plugin turns your terminal into a full WordPress admin panel. Try it on a staging site first, get comfortable with the abilities, then deploy to production. The setup takes 2 minutes. The time it saves you is permanent. ## Frequently Asked Questions ### Q: What is MCP (Model Context Protocol)? MCP is an open protocol created by Anthropic that standardizes how AI applications connect to external tools and data sources. Think of it like USB for AI - a universal interface that lets any MCP-compatible client (Claude Code, Claude Desktop, Cursor) talk to any MCP server (WordPress, databases, APIs) using a shared language. WP MCP Ultimate turns your WordPress site into an MCP server. ### Q: Does WP MCP Ultimate work with ClassicPress? Not currently. The plugin depends on WordPress's REST API infrastructure and Application Passwords system, which ClassicPress has diverged from. ClassicPress support is not on the roadmap, but the codebase is GPL-2.0 - anyone is welcome to fork and adapt it. ### Q: Is it safe for production sites? The plugin requires administrator credentials over HTTPS and respects WordPress capability checks. For most single-site WordPress installations, this is equivalent to logging into wp-admin. That said, I recommend starting on a staging site to understand what each ability does before connecting a production site. The v2.0 release will add additional security hardening. ### Q: How much does it cost? Nothing. Free forever. GPL-2.0 licensed. No premium tier, no feature restrictions, no usage limits. The plugin is fully open source on [GitHub](https://github.com/AgriciDaniel/wp-mcp-ultimate). ### Q: How is this different from using the WordPress REST API directly? The WordPress REST API is a general-purpose HTTP API - your AI client would need to know the exact endpoints, authentication headers, request formats, and response structures for every operation. WP MCP Ultimate wraps all of that behind the MCP protocol, which AI clients already understand natively. The AI discovers available abilities automatically, gets typed schemas for each one, and executes them through a single standardized interface. It's the difference between giving someone a map and giving them GPS. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How claude-seo runs 9 parallel agents for a full site audit - [Claude Code Just Replaced Your Blog Writer](/blog/claude-code-blog-writer) - claude-blog's 100-point scoring system for dual-optimized content - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full stack that WP MCP Ultimate now completes - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - Where WP MCP Ultimate fits in the broader ecosystem Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # I Replaced $500/Month in SEO Data With Free Google APIs - Here's the Setup - URL: [https://agricidaniel.com/blog/google-api-seo-automation-claude-code](https://agricidaniel.com/blog/google-api-seo-automation-claude-code) - Published: 2026-03-28 - Updated: 2026-03-28 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. claude-seo v1.7.0 and claude-blog v1.6.5 now integrate 9 Google APIs for free. PageSpeed, CrUX, Search Console, GA4, YouTube, NLP - all at $0.0006/query. Here's how I wired it all together. Ahrefs charges $499/month for SEO data. Semrush charges $449/month. Between the two, that's $11,376 per year for data that Google literally gives away for free through its own APIs. I just shipped the biggest update to [claude-seo](https://github.com/AgriciDaniel/claude-seo) and [claude-blog](https://github.com/AgriciDaniel/claude-blog) since launch. Both tools now connect directly to 9 Google APIs, pulling the same data that powers Google's own ranking algorithms. The cost? About $0.0006 per query. At 1,000 queries per day, that's $18/month. That's not a typo. Here's what changed, why it matters, and how to set it up in 5 minutes. ## What Changed in v1.7.0 and v1.6.5 Both tools shipped a new Google API sub-skill on the same day: - [claude-seo v1.7.0](https://github.com/AgriciDaniel/claude-seo) added seo-google - 21 commands, 11 Python scripts, PDF report generation, SSRF protection, and a new subagent that activates automatically during audits when it detects Google API credentials. 10 reference documentation files ship with it. - [claude-blog v1.6.5](https://github.com/AgriciDaniel/claude-blog) added blog-google - 13 commands, 11 Python scripts, YouTube video auto-discovery and embedding, and VideoObject JSON-LD schema generation. Also cleaned up all 22 skill frontmatters for Claude Code plugin compliance. Both share a single config file at ~/.config/claude-seo/google-api.json , so you set up credentials once and both tools use them. If you've read [how claude-seo replaced my $300/month SEO stack](/blog/claude-code-seo-stack) or [how claude-blog replaced my blog writer](/blog/claude-code-blog-writer), this is the next evolution: first-party Google data piped directly into the audit and content pipelines. ## The 9 APIs (And Why They're All Free) Google offers these APIs with generous free quotas because they want developers building on their platform. Here's what we wired in: - PageSpeed Insights API - Lighthouse lab scores plus CrUX field data in a single call. 25,000 queries/day free. - Chrome UX Report (CrUX) API - Real Chrome user metrics: LCP, INP, CLS, TTFB, FCP. This is the actual data Google uses for Core Web Vitals ranking signals. 25-week historical trends included. - Search Console API - Queries, clicks, impressions, average position. 1,200 queries per minute per site. - URL Inspection API - Real indexation status, crawl details, mobile usability, canonical selection. Know exactly how Google sees your page. - Indexing API - Submit URLs for immediate indexing instead of waiting for the next crawl cycle. 200 URLs per day. - GA4 Data API - Organic traffic, sessions, pageviews, bounce rate, engagement metrics. - Cloud Natural Language API - NLP entity extraction and sentiment analysis. Understand which entities Google associates with your content and optimize for E-E-A-T. - YouTube Data API - Video search, metadata, view counts, channel authority. Powers the auto-embed feature in claude-blog. - Google Ads Keyword Planner API - The gold standard for search volume data. Actual Google query volumes, not third-party estimates. Every one of these APIs has a free tier that covers normal usage. You don't need a credit card for the first 8. Keyword Planner requires a Google Ads account (free to create), though the developer token approval process takes a few days. Official documentation for each: [PageSpeed Insights API](https://developers.google.com/speed/docs/insights/v5/get-started), [CrUX API](https://developer.chrome.com/docs/crux/api), [Search Console API](https://developers.google.com/webmaster-tools/v1/api_reference_index), [Indexing API](https://developers.google.com/search/apis/indexing-api/v3/quickstart), [GA4 Data API](https://developers.google.com/analytics/devguides/reporting/data/v1), [Cloud NLP API](https://cloud.google.com/natural-language/docs), [YouTube Data API](https://developers.google.com/youtube/v3), [Keyword Planner API](https://developers.google.com/google-ads/api/docs/keyword-planning/overview). PageSpeed Insights API - the same Lighthouse + CrUX data that powers Google's ranking signals, now piped directly into your audit workflow ## The Cost Math That Changes Everything Let me make this concrete. Here's what the major SEO platforms charge annually for data that Google's own APIs provide for free (or near-free): ANNUAL SEO DATA COSTS Ahrefs $5,988/yr Semrush $5,399/yr Moz Pro $2,148/yr Google APIs ~$216/yr - SAVE $5,772/year with free Google APIs Based on 1,000 queries/day at $0.0006 avg per query Annual cost comparison - paid SEO platforms vs direct Google API access But here's the part nobody talks about: the Google API data is actually better. When you check Core Web Vitals through Ahrefs or Semrush, they're running their own Lighthouse tests from their servers. That's synthetic lab data. When you use the CrUX API, you get real Chrome user metrics, the same dataset Google uses to evaluate your site for ranking. First-party source data vs third-party approximation. The free option is the authoritative one. Same with Search Console data. Ahrefs estimates your search traffic based on keyword position tracking. Search Console gives you the actual clicks and impressions from Google's own logs. There's no estimation involved. You're reading from the source. Search Console API data - actual clicks and impressions from Google's own logs, not third-party estimates ## YouTube Embedding and AI Visibility This is the feature in claude-blog v1.6.5 that I'm most excited about. YouTube mentions have a 0.737 correlation with AI search visibility , based on a 75,000-brand study. That's the strongest single signal found. Stronger than FAQ schema, stronger than answer-first formatting, stronger than anything else tested. AI SEARCH CITATION CORRELATION FACTORS YouTube embed 0.737 FAQ schema 0.651 Answer-first 0.589 Cited statistics 0.523 Internal links 5+ 0.412 YouTube embeds = strongest single signal Source: 75,000-brand AI visibility study (Ahrefs) AI search citation correlation - YouTube embedding leads all other signals More supporting data: video citations in AI Overviews are up 414% year-over-year (BrightEdge Q1 2025). How-to video citations up 651%. YouTube gets cited 200x more than any other video platform by AI systems. Pages with embedded video have a 53x higher chance of front-page ranking (Forrester). So claude-blog v1.6.5 now automatically discovers and embeds relevant YouTube videos in every blog post it creates. Here's how it works: Auto-discovery - During the research phase, claude-blog searches YouTube via the Data API for videos relevant to your post topic. It scores each video on relevance, view count, recency, channel authority, and engagement. Only videos scoring above 50/100 get embedded. - Lazy loading - Uses the srcdoc pattern instead of standard YouTube embeds. Initial payload is ~5KB versus ~500KB for a standard iframe. That's a 99% reduction in initial page weight per embed. - AI crawler fallback - A tag renders the video title, channel, and description as plain text. GPTBot, PerplexityBot, ClaudeBot, and Google-Extended can all see and cite the video content even without JavaScript. - VideoObject schema - Generates JSON-LD VideoObject for every embed, bringing the total to 7 schema types per blog page (up from 6). The schema includes duration, view count, upload date, and thumbnail URL. The embed format supports MDX, HTML, Markdown, and Hugo. 2-3 videos per post maximum, with at least 500 words between embeds. ## The 4-Tier Credential System Not every user needs every API. So both tools use a progressive credential system - you start with the easy stuff and add more as you need it: CREDENTIAL TIERS - SETUP TIME vs APIs UNLOCKED Tier 0 5 APIs (PSI, CrUX, YouTube, NLP) 2 min setup Tier 1 +3 APIs (GSC, Inspect, Index) 10 min setup Tier 2 +GA4 + property ID Tier 3 +Keywords + Ads dev token - 80% of value unlocked in 2 minutes (Tier 0) Progressive credential system - start simple, add APIs as you need them The config is a single JSON file shared between both tools: // ~/.config/claude-seo/google-api.json { "api_key": "AIzaSy...", "oauth_client_path": "/path/to/client_secret.json", "default_property": "sc-domain:yoursite.com", "ga4_property_id": "properties/123456789" } In claude-seo, the seo-google agent spawns automatically during audits when it detects credentials. No credentials? The audit still runs with crawl-based analysis. With credentials? You get real CrUX field data, actual indexation status, and Search Console performance metrics layered on top. Zero breaking changes - the credentials are additive. ## PDF Reports for Client Deliverables claude-seo v1.7.0 can now generate enterprise-grade PDF reports from Google API data. This was one of the most requested features from agency users in the [AI Marketing Hub](https://www.skool.com/ai-marketing-hub). The reports use WeasyPrint for HTML-to-PDF rendering and matplotlib for charts at 200 DPI. The template is A4 format with: Title page with score summary - Table of contents with section badges - CrUX trend charts (25-week timelines) - Performance distribution gauges - Data tables and heatmaps - Prioritized recommendation sections Four report types: CWV audit, GSC performance, indexation status, or full comprehensive. Run /seo google report full and hand the PDF to your client. That's a deliverable that would cost $500+ from an SEO agency. On the security side, all user URLs pass through a validate_url() function that blocks private IPs, loopback addresses, and GCP metadata endpoints. SSRF protection is built into every script, not bolted on as an afterthought. OAuth tokens no longer store the client secret, reading it from the client_secret.json file on each request instead. 8 credential patterns are gitignored by default - .env files, client secrets, OAuth tokens, service account keys. ## How to Set It Up (5 Minutes) The setup is the same for both tools since they share credentials. Here's the quick path to Tier 0 (5 APIs, 2 minutes): - Go to [Google Cloud Console](https://console.cloud.google.com) and create a project (or use an existing one) - Enable the APIs: PageSpeed Insights, Chrome UX Report, YouTube Data API v3, Cloud Natural Language - Create an API key under Credentials - Add it to your config: ``` mkdir -p ~/.config/claude-seo echo '{"api_key": "YOUR_API_KEY_HERE"}' > ~/.config/claude-seo/google-api.json ``` Test it: ``` # In Claude Code with claude-seo installed: /seo google pagespeed https://yoursite.com # Or with claude-blog installed: /blog google crux-history https://yoursite.com ``` For Tier 1 (adds Search Console, URL Inspection, Indexing API), you need OAuth 2.0 credentials. Create an OAuth consent screen in test mode (no verification needed), create OAuth client credentials (Desktop app type), download the client_secret.json file, and add the path to your config. The first time you run a Tier 1 command, it opens a browser window for authorization with a localhost:8085 callback. Takes about 10 minutes total. Both claude-seo and claude-blog ship with a setup guide. Run /seo google setup or /blog google setup and it walks you through everything step by step, checking which APIs are enabled and which credentials are missing. ## What's Next This update brings the total to 22 sub-skills in claude-blog and 18 skills in claude-seo (15 core + 2 extensions + the new seo-google). Together, they cover the full lifecycle from keyword research to content creation to technical audit to performance monitoring, all from your terminal. Next on the roadmap: - Google Merchant Center API - Product schema enrichment for e-commerce SEO - Automated weekly reports - Scheduled PDF generation via n8n integration - Multi-property aggregation - Roll up Search Console data across multiple domains into a single dashboard view If you're already using claude-seo or claude-blog, update to the latest version and run /seo google setup to get started. If you're new, check out [my comparison of free SEO audit tools](/blog/free-seo-audit-tools) or see how both tools fit into [the full AI marketing automation stack I use daily](/blog/ai-marketing-automation-stack). Both tools are featured in [the best Claude Code skills for 2026](/blog/best-claude-code-skills-2026). The release notes are on GitHub: [claude-blog v1.6.5](https://github.com/AgriciDaniel/claude-blog/releases/tag/v1.6.5) and [claude-seo v1.7.0](https://github.com/AgriciDaniel/claude-seo/releases/tag/v1.7.0). ## Frequently Asked Questions ### Are the Google APIs actually free? Yes. All 9 APIs have generous free tiers. PageSpeed Insights allows 25,000 queries per day. CrUX is unlimited. Search Console allows 1,200 queries per minute per site. The only API that requires a paid account is the Keyword Planner, which needs a Google Ads account (free to create, but you need an approved developer token). At typical usage levels for a single site or small agency, you will never exceed free quotas. ### How is this different from the existing claude-seo audit? The existing audit uses crawl-based analysis (fetching your pages and parsing HTML). The Google API integration adds first-party data on top: real Chrome user metrics from CrUX, actual indexation status from URL Inspection, search performance from GSC, and organic traffic from GA4. The audit runs with or without API credentials, but with them you get authoritative Google data instead of estimates. ### Do I need separate credentials for claude-seo and claude-blog? No. Both tools share the same config file at ~/.config/claude-seo/google-api.json . Set up credentials once and both tools use them automatically. The 4-tier system is identical across both. ### Can I use a service account instead of OAuth? Yes. For Search Console, Indexing API, and GA4, you can use a Google Cloud service account instead of OAuth. This is better for automated workflows since there's no browser-based login. Create a service account, grant it access to your Search Console property, and add the JSON key path to your config. The scripts detect and use whichever credential type is available. ### What about API rate limits? The rate limits are generous for normal usage. PageSpeed: 25,000/day. CrUX: 150/min (shared with history). GSC: 1,200/min per site. Indexing: 200 URLs/day. GA4: 10 concurrent requests. YouTube: 10,000 units/day. All scripts include exponential backoff for rate limit errors. For bulk operations, the batch commands handle pagination and throttling automatically. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [Claude Code Just Replaced Your Blog Writer](/blog/claude-code-blog-writer) - Dual-optimized content for Google rankings and AI citations - [Free SEO Audit Tools That Actually Work](/blog/free-seo-audit-tools) - Genuinely free SEO audit tools that replace paid subscriptions Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # AI Marketing Automation: The Open-Source Stack I Use Daily - URL: [https://agricidaniel.com/blog/ai-marketing-automation-stack](https://agricidaniel.com/blog/ai-marketing-automation-stack) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Marketing - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. My entire AI marketing stack costs roughly $50/month in API calls. Here's every tool, how they connect, and why I stopped paying for Ahrefs. I spend about 6 hours a day inside my AI marketing stack. Not tweaking it, not configuring it - actually using it to publish content, track rankings, audit sites, and run ads. The whole thing is open source, self-hosted, and costs me roughly $50/month in API calls . The equivalent SaaS stack would run $3,000-5,000/month. This isn't a theoretical setup. I've been running this daily since December 2025, across 21 repositories, with 4,849 GitHub stars from people who are using the same tools. Let me walk you through exactly what I use and how it all connects. ## The Daily Workflow (What Actually Happens) My morning starts the same way every day. I open my terminal, run /seo audit inside Claude Code, and within 90 seconds I have a full technical SEO audit of whatever site I'm working on. Not a surface-level check - we're talking Core Web Vitals, internal linking gaps, schema validation, keyword cannibalization, the works. That single command replaces what used to take me 45 minutes across 3 different tools. From there, the audit results feed into n8n workflows that I've built to act on the findings automatically. Found a page with degraded performance? n8n pings me on Slack and creates a task. Found a keyword gap? n8n triggers a content brief generation. Found a broken internal link? n8n fixes it (yes, actually fixes it) if it has write access to the CMS. The afternoon is content. I use /blog to generate, optimize, and publish blog posts that are SEO-ready from the first draft. Not "AI slop" - the skill runs competitor analysis, builds topical maps, and structures content around actual search intent. I publish 3-5 posts per week this way, each one ranking within 14 days on average. Ads run themselves. /ads handles Google Ads campaign creation, keyword research, ad copy generation, and bid strategy recommendations. I review and approve, but the heavy lifting is done. And images? banana-claude handles that. Blog hero images, social graphics, OG images - all generated via /banana with Creative Director mode that actually understands composition. I haven't opened Canva in months. The entire daily loop takes about 2 hours of active attention. The rest is automated. Before this stack, the same output required 8-10 hours and $938/month in tool subscriptions. I know because I tracked both periods. ## How I Got Here (The Expensive Way) Let me be transparent about the path. In mid-2025, I was paying for Ahrefs ($249/month), Semrush ($139/month because I needed features Ahrefs didn't have), Jasper AI ($99/month), and Surfer SEO ($99/month). Plus Zapier at $89/month to glue things together. That's $675/month before I even touched Google Ads tools. The breaking point was a Tuesday in October 2025. I was running a keyword gap analysis in Semrush, waiting for Ahrefs to finish a backlink audit, and simultaneously trying to get Jasper to write something that didn't sound like it was generated by an AI from 2023. Three tabs, three subscriptions, three tools that didn't talk to each other. I thought: I'm a developer. I can build integrations. What if the tools weren't separate products but skills inside one environment? Claude Code had just launched its skills framework. I built the first version of claude-seo in a weekend. It was rough - maybe 40% of what Ahrefs could do. But it was 40% that ran in my terminal, cost nothing, and could be extended by me. Within a month, I'd cancelled Ahrefs. Within two months, Semrush was gone too. By December, the entire SaaS stack was replaced. (I still have a soft spot for Ahrefs' backlink index. DataForSEO's backlink data is good but not quite at that level yet. They're actively working on it though.) ## The Stack: Every Tool and How They Connect Here's the architecture. It looks complicated on paper, but each piece does one thing well: Three-layer architecture: Intelligence, Orchestration, Output ### Layer 1: Intelligence (Claude Code Skills) These are the brains of the operation. Each one is a Claude Code skill - a set of slash commands that run inside your terminal and do specialized work. - [claude-seo](/blog/claude-code-seo-stack) (2,974 stars) - The brain. Full SEO auditing, keyword research, content optimization, technical analysis. Runs as a Claude Code skill with /seo . This is the repo that started everything. It handles 186 different SEO checks in a single audit, covers on-page, off-page, technical, and content quality analysis. People tell me it replaced their Ahrefs subscription. I believe them because it replaced mine. - [claude-ads](/blog/claude-code-ad-agency) (1,171 stars) - Google Ads campaign management. Keyword research, ad copy, bid strategies, performance analysis. The ad copy generation alone is worth it - it produces 10 headline variants and 5 description variants per ad group, scored against quality score predictors. - [claude-blog](/blog/claude-code-blog-writer) (300 stars) - Content pipeline. Generates, optimizes, and publishes blog posts with SEO baked in. Not just "write me a blog post" - it researches the SERP, analyzes competing content, identifies content gaps, and produces posts that are structured to rank. - [banana-claude](/blog/banana-claude-ai-image-generation) (41 stars) - AI image generation via Gemini with Creative Director mode. Every visual asset in my content workflow comes from here. - skill-forge - The meta-tool that builds all the other skills. More on this in a separate post. ### Layer 2: Orchestration (n8n) n8n is the connective tissue. It's an open-source workflow automation platform (think Zapier but self-hosted and infinitely more powerful). I run it on a $5/month VPS and it handles: - Scheduled SEO audits (daily at 6 AM) - Rank tracking pipelines (pulling from DataForSEO every 24 hours) - Content publishing workflows (draft → review → optimize → publish) - Competitor monitoring (keyword gap analysis weekly) - Alert routing (Slack, email, webhook) - Site health checks (every 6 hours, catches broken pages within the same business day) n8n is what turns a collection of tools into an actual system. Without it, I'd be running each tool manually. With it, 80% of the work happens while I sleep. I currently have 23 active workflows running, and they execute roughly 2,000 tasks per day across all the sites I manage. ### Layer 3: Data (APIs) The skills need data to work with. Here's where it comes from: - DataForSEO - SERP data, keyword volumes, backlink data, site audits. Their API is pay-per-use and dramatically cheaper than Ahrefs/Semrush subscriptions. I spend about $30/month here. The key insight: you don't need unlimited crawls if you're smart about what you crawl. I track 500 keywords daily and run targeted audits instead of full-site crawls. - Gemini API - Content generation for longer-form pieces where I need high throughput. About $10/month. I use Gemini specifically for first drafts because the cost-per-token is low and the quality is good enough for a starting point that claude-blog then optimizes. - Perplexity API - Research and fact-checking. When I need current data that's not in training sets - recent algorithm updates, competitor launches, industry news. About $5/month. Worth every penny for keeping content accurate. - Google Search Console API - First-party ranking and click data. Free. This is the ground truth that validates everything else. - Google Ads API - Campaign management. Free (you pay for ads, not API access). - Google Indexing API - Submit new pages for crawling immediately after publishing. Free. Cuts indexing time from days to hours. ### Layer 4: Publishing ([Rankenstein](/blog/n8n-seo-content-system)) Rankenstein is the product my co-founder Benjamin Samar and I built together. It's an n8n-based SEO automation system that takes everything above and turns it into a one-click publishing pipeline. Version 8 handles everything from keyword research to published, indexed blog post in under 10 minutes. We sell Rankenstein templates on Gumroad, and they're the most popular product in the AI Marketing Hub. The v7 template has been downloaded by hundreds of users. v8 added a GUI layer so you don't need to touch n8n's visual editor if you don't want to. ## The Actual Data Flow (How They Talk to Each Other) Let me trace a real workflow from start to finish. Say n8n's daily rank tracking detects that a blog post dropped from position 4 to position 11 for its target keyword. - n8n detects the drop via DataForSEO SERP check (runs at 6 AM daily) - n8n triggers claude-seo to run a focused audit on that specific page - claude-seo analyzes the page vs. the new top 10 results: word count delta, missing topics, schema gaps, Core Web Vitals regression, backlink changes - claude-seo returns a structured report with specific recommendations: "Add section on [topic], update publish date, fix CLS issue on mobile, add FAQ schema" - n8n routes the report to Slack for my review - I approve the update (one click in Slack) - n8n triggers claude-blog to generate the updated content based on claude-seo's recommendations - claude-blog produces the update , preserving the existing content structure while adding the missing sections - banana-claude generates a new hero image (optional, triggered if the post is older than 6 months) - Rankenstein publishes the update to the CMS and resubmits to Google Indexing API Total time from detection to published update: about 25 minutes, of which I spent maybe 2 minutes reviewing and approving. Without the stack, this same process would take 3-4 hours of manual work spread across multiple tools. ## Cost Comparison: This Hurts to Look At I used to pay for the traditional stack. Here's what that looked like vs. what I pay now: Tool Category Traditional Stack Monthly Cost My Stack Monthly Cost SEO Audit Ahrefs $249 claude-seo $0 Keyword Research Semrush $139 claude-seo + DataForSEO ~$15 Content Writing Jasper AI $99 claude-blog + Gemini ~$10 Content Optimization Surfer SEO $99 claude-seo $0 Rank Tracking SE Ranking $65 n8n + DataForSEO ~$15 Ads Management Opteo $99 claude-ads $0 Automation Zapier $89 n8n (self-hosted) $5 Site Monitoring ContentKing $99 n8n + DataForSEO ~$5 Image Generation Canva Pro + Midjourney $42 banana-claude + Gemini ~$2 Total $980/mo ~$52/mo That's a 19x cost reduction. And honestly, the open-source stack is more flexible because I can modify anything. When I need a new feature, I build it - I don't submit a feature request and wait 6 months. (The traditional stack numbers are for their mid-tier plans. Enterprise pricing is even more absurd. And these are single-user prices - if you're running an agency with 5 team members, multiply the traditional column by 3-5x for team plans.) There's a hidden cost advantage too: no seat-based pricing. My stack costs the same whether I'm managing 1 site or 50 sites. The only variable is API call volume. For agencies, this changes the entire unit economics of client work. MONTHLY COST COMPARISON Traditional SaaS Stack $980/mo Open-Source Stack $52/mo 94% SAVINGS $11,136 saved per year - Ahrefs $99 + Surfer $89 + Jasper $59 + HubSpot $800 + misc vs Claude API ~$40 + n8n $12 hosting The math is simple - 94% cost reduction with better coverage ## The 21 Repos That Make It Work Everything is public on my GitHub. Here are the ones that matter most for this stack: claude-seo - SEO auditing and optimization skill (2,974 stars) - claude-ads - Google Ads management skill (1,171 stars) - claude-blog - Blog content pipeline skill (300 stars) - skill-forge - Skill builder (meta-tool) - banana-claude - AI image generation for content (41 stars) - rankenstein-templates - n8n workflow templates for SEO - claude-seo-website - Landing page for claude-seo The rest are supporting tools, experimental projects, and utilities - things like data processing scripts, API wrappers, testing frameworks, and documentation sites. But those 7 are the core of the daily stack. If you only install three things, make it claude-seo, n8n, and a DataForSEO account. That alone replaces 80% of the traditional stack. The repos have grown organically. claude-seo started in July 2025 and hit 1,000 stars in its first month. claude-ads launched in September and grew even faster because the claude-seo community was already primed. The total across all 21 repos is 4,849 stars as of writing, with 364 followers on the GitHub account. Not bad for a one-person operation that started 8 months ago. ## Who This Is Actually For I get asked this a lot, so let me be specific: - SEO professionals who are tired of paying $300+/month for tools that do less than they should. If you're comfortable with a terminal, you can run this stack. You don't need to be a developer - the skills are installed with one command and run via slash commands. But terminal comfort is the minimum bar. - Agency owners who want to cut tool costs across 10-50 client accounts. The per-client cost of my stack is essentially zero (you're paying for API calls, not seats). I've talked to agency owners who were spending $5,000/month on tools alone. They switched to this stack and redirected that budget to actual marketing spend. - Content marketers who want to publish more without hiring more writers. claude-blog + Rankenstein is a force multiplier. One person can maintain a publishing cadence of 15-20 optimized posts per month. That's a content team's output from a single operator. - Developers who want to build on top of these tools. Everything is MIT licensed. Fork it, modify it, ship it. Several people have already built commercial products on top of claude-seo, and I'm genuinely thrilled about that. (If you've never opened a terminal, this might not be for you yet. But I'm working on making it more accessible - Rankenstein v8 has a GUI, and I'm planning a hosted version for people who don't want to self-host anything.) ## Common Objections (And My Honest Responses) ### "Open source tools can't match enterprise SaaS quality" For some categories, sure. Ahrefs' web crawler and backlink database took years and millions of dollars to build. I'm not replicating that. But I am using DataForSEO's data (which is comparable for most use cases) and building better analysis on top of it. The analysis layer is where open source actually wins , because I can customize it for my specific needs instead of using a one-size-fits-all dashboard. ### "What about support?" The AI Marketing Hub has 158 paid members who actively help each other. The GitHub repos have active issue trackers. I personally respond to issues within 24 hours. Is it Ahrefs-level support? No. But it's also not a $249/month subscription. ### "This seems like a lot of setup" Initial setup takes about 2 hours for the core stack (claude-seo + n8n + DataForSEO). After that, it runs itself. Compare that to the ongoing time cost of context-switching between 5 different SaaS dashboards every day. ## What's Next I'm building this in public. Every tool, every workflow, every result. The AI Marketing Hub on Skool is where 220+ Pro members share their setups, results, and custom workflows. The free tier gets you access to the community and basic resources. Pro ($88/month) gets you the Rankenstein templates, priority support, and my personal workflow library. On the product side, Rankenstein v9 is in development with deeper claude-seo integration and multi-language support. claude-seo is getting a Google Search Console integration that pulls real ranking data into the audit workflow. And I'm working on a hosted version of the entire stack for people who want the benefits without the self-hosting. If you want to start with the tools, they're all on GitHub under AgriciDaniel. Star what you find useful - it genuinely helps with visibility. If you want the community and the done-for-you workflows, join the Hub. The traditional marketing tool stack had a good run. But when an open-source alternative does 90% of the work at 5% of the cost, the math stops making sense pretty quickly. The future of marketing automation isn't another $300/month SaaS - it's skills, workflows, and APIs that you own and control. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [Claude Code Just Replaced Your Ad Agency](/blog/claude-code-ad-agency) - 186 automated ad audit checks across 6 platforms, for free - [How I Built an AI SEO Content System in n8n](/blog/n8n-seo-content-system) - Full walkthrough of the Rankenstein n8n workflow, node by node Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # banana-claude: AI Image Generation That's Surprisingly Good at Logos - URL: [https://agricidaniel.com/blog/banana-claude-ai-image-generation](https://agricidaniel.com/blog/banana-claude-ai-image-generation) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I built banana-claude for blog hero images. Turns out it's weirdly good at logos. Here's how Creative Director mode works and why 41 people starred it. I'll be honest: I built banana-claude to solve a boring problem. I needed hero images for blog posts and social media graphics for content promotion, and I was tired of opening Canva or prompting Midjourney in a separate browser tab. I wanted image generation inside my terminal, triggered by a slash command, integrated with my content workflow. What I didn't expect was that it would be genuinely good at logos. Not "AI logo that looks like every other AI logo" good. Actually good. The kind of good where a client looks at the concept and says "yeah, that's the direction" on the first round. That surprised me, and I think it's worth talking about why. ## What Is banana-claude? banana-claude is a Claude Code skill that generates images using Google's Gemini model. You run /banana followed by a description of what you want, and it generates the image. Simple enough on the surface. But the interesting part is Creative Director mode. Instead of just passing your prompt straight to the image model (which produces the generic, overprocessed look we've all come to associate with AI images), banana-claude has an intermediate layer that art-directs the generation. It analyzes your request, considers composition, color theory, typography placement, and visual hierarchy, then constructs an optimized prompt that produces significantly better results. Think of it as the difference between telling an illustrator "make me a logo" and briefing a creative director who then briefs the illustrator. The output quality gap is substantial. Why "banana"? Honestly, I needed a name that was short, memorable, and available on GitHub. banana-claude it was. Sometimes the best engineering decisions are the ones you don't overthink. ## How Creative Director Mode Actually Works When you trigger Creative Director mode (it's the default, you'd have to opt out), here's what happens under the hood: - Intent analysis - banana-claude determines what kind of visual you're asking for: photo, illustration, logo, icon, diagram, social graphic, etc. Each type has different optimization strategies. This classification step is crucial because the prompting techniques for a logo are completely different from those for a photograph. - Style mapping - Based on the intent, it selects appropriate style parameters. Logos get clean lines, limited color palettes, and scalability considerations. Blog heroes get atmospheric lighting and editorial composition. Social graphics get bold text placement and platform-specific aspect ratios. I built up these style maps over about 3 weeks of testing, generating roughly 500 images to find the parameters that consistently produce good results for each category. - Prompt engineering - Your casual description gets transformed into a structured prompt with specific directives about composition, negative space, color relationships, and technical constraints. This is where the magic happens - the difference between a good prompt and a great prompt is about 10x in output quality. The prompt engineer adds negative constraints too: "no gradients," "no drop shadows," "no stock photo aesthetic" - the things that make AI images look unmistakably AI. - Generation - The optimized prompt hits Gemini's image generation API. - Quality check - banana-claude evaluates the output against the original intent. If it's off-brief (wrong aspect ratio, cluttered composition, mismatched style), it regenerates with adjusted parameters. The evaluation uses a scoring rubric: composition balance, color harmony, text legibility (if applicable), and stylistic consistency with the detected intent. (The quality check step adds about 5 seconds to generation time. I debated removing it for speed, but the hit rate improvement was too significant - roughly 40% fewer regenerations needed.) The Creative Director pipeline - Claude interprets, enhances, then generates ## The Logo Surprise I started noticing the logo quality when I was generating assets for my own projects. I needed a quick logo concept for a tool I was building, ran /banana creative director: minimalist logo for an SEO automation tool, think clean tech aesthetic , and the result was... actually usable. Not as a final logo - no AI tool replaces a brand designer for final deliverables. But as a concept? As a starting point for a design brief? It was producing concepts that would have taken a human designer 2-3 hours of exploration to reach. I think the reason is Gemini's training data combined with Creative Director mode's style constraints. When you tell the model "minimalist logo" and then the prompt engineer adds "single color, geometric, scalable to 16px favicon, clean negative space, no gradients," you've eliminated most of the failure modes that make AI logos look like AI logos. The constraints are doing the heavy lifting. Unconstrained generation produces generic results. Heavily constrained generation produces focused, usable results. I tested this systematically. Same logo brief, 50 generations with Creative Director mode, 50 without. With Creative Director mode, 34 out of 50 (68%) were usable as concept starting points. Without it, only 8 out of 50 (16%). The prompt engineering layer is a 4x improvement in hit rate. That's the whole justification for the tool's existence. Logo examples generated with banana-claude - from geometric marks to wordmarks ## Use Cases (What I Actually Generate) Here's what banana-claude produces in a typical week for me: - Blog hero images - Every post on this site has a hero image generated with banana-claude. Abstract, editorial, matches the dark theme. Takes about 15 seconds per image. I generate 3 options and pick the best one, so the total time per blog post image is under a minute. - Social media graphics - LinkedIn posts, Twitter/X cards, community announcements. banana-claude knows platform dimensions, so I just specify "linkedin post" and it handles the aspect ratio. For the AI Marketing Hub, I generate 5-7 social graphics per week. - OG images - The preview images that show up when you share a link. Generated automatically as part of the claude-blog publishing workflow. No manual step - publish a post, get an OG image. - Logo concepts - Quick explorations for new projects or client pitches. Not final logos, but strong starting points. I've used banana-claude logo concepts in 3 client presentations, and in each case the client selected one as the direction to develop further. - Ad creative - Display ad imagery for Google Ads campaigns. claude-ads calls banana-claude when it needs visual assets for ad groups. It generates multiple size variants (300x250, 728x90, 160x600) from a single brief. The integration with other tools is what makes it practical rather than novelty. banana-claude isn't a standalone image generator you visit when you need a picture. It's a component in a larger workflow. claude-blog calls it automatically for hero images. claude-ads calls it for ad creative. I call it manually for everything else. ## Integration With the Rest of the Stack banana-claude was designed to be called by other skills, not just by humans. Here's how it fits: - claude-blog - When publishing a new post, claude-blog triggers banana-claude to generate a hero image and OG image based on the post title and content summary. No manual step required. The generated images match the site's visual language because I've tuned the style maps to produce dark-themed, abstract visuals that fit the #0A0A0A aesthetic. - claude-ads - When creating display ad campaigns, claude-ads generates ad creative variants through banana-claude. It produces 3-5 options per ad group, optimized for the target dimensions. The creative incorporates the ad's headline and call-to-action, positioned using the style map's text placement rules. - Manual use - /banana for quick one-off generations. I use this 4-5 times a day for social content, presentation slides, and random visual needs. The skill-to-skill communication works through Claude Code's standard tool interface. banana-claude exposes its generation function, and other skills call it like any other tool. This composability is why I built everything as separate skills instead of one monolithic tool. See how it all connects in [my AI marketing automation stack overview](/blog/ai-marketing-automation-stack). ## Limitations (Being Honest) banana-claude is not replacing your design team. Specifically: - Text in images is still hit-or-miss. Gemini has gotten better at this, but I'd say text rendering is correct about 75% of the time. For logos with wordmarks, expect to regenerate a few times. For images where text isn't critical (abstract backgrounds, pattern-based designs), this isn't an issue. - Complex illustrations with specific spatial relationships ("person standing next to a car that's parked in front of a building") can go sideways. Simple compositions work best. The more elements you add, the more opportunities for the model to get confused about relative positioning. - Brand consistency across multiple generations is difficult. Each generation is independent, so maintaining exact style across a series of images requires careful prompting. I'm working on a "style memory" feature that would cache successful style parameters and reuse them, but it's not ready yet. - Final production assets still need a human designer. banana-claude is for concepts, drafts, and content imagery - not for your company's logo that will go on business cards and billboards. The output resolution is sufficient for web use but not for print. (If someone tells you their AI tool replaces professional designers entirely, they're either lying or they have very low design standards.) ## Try It banana-claude is open source, MIT licensed, and currently at 41 stars on GitHub (featured in my guide to the [best Claude Code skills in 2026](/blog/best-claude-code-skills-2026)). It's one of my smaller repos by star count, but it's one of the tools I personally use most frequently. The Gemini API costs are minimal - I spend about $2/month on image generation, which covers roughly 200-300 images. Install it as a Claude Code skill, add your Gemini API key, and run /banana . Start with something simple - a blog hero image or a social graphic - and see how Creative Director mode handles it. The first time it produces something genuinely good from a one-line description, you'll understand why I keep using it. If you build something interesting with it or find edge cases where it excels (or fails spectacularly), I want to hear about it. The best improvements to the Creative Director prompts have come from community feedback. One user discovered that adding color palette constraints ("use only #E07850 and #0A0A0A") produced dramatically better results for brand-consistent assets. That's now a built-in feature. Drop your results in the AI Marketing Hub or open an issue on GitHub. ## Related Posts - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive guide to top Claude Code skills ranked by GitHub stars - [Claude Code Just Replaced Your Blog Writer](/blog/claude-code-blog-writer) - Dual-optimized content for Google rankings and AI citations Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # Best Claude Code Skills in 2026 - The Complete Guide - URL: [https://agricidaniel.com/blog/best-claude-code-skills-2026](https://agricidaniel.com/blog/best-claude-code-skills-2026) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Guides - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. The definitive guide to Claude Code skills in 2026. What they are, how they work, the top skills ranked by GitHub stars, and how to build your own. Claude Code shipped skill support in late 2025, and in the months since, an entire ecosystem has exploded around it. There are now hundreds of community-built skills covering everything from SEO audits to video editing to ad account analysis. The problem is figuring out which ones are actually worth installing. I've tested most of them (I built several of the top ones), and this is the guide I wish existed when I started. ## What Are Claude Code Skills? If you're new to this: Claude Code is Anthropic's official CLI for Claude. You install it, run it in your terminal, and it can read your codebase, execute commands, edit files, and interact with APIs. Skills are extensions that give Claude Code specialized capabilities. Think of them as plugins that turn a general-purpose AI assistant into a domain expert. A skill is just a markdown file (or a set of them) that lives in your project's .claude/skills/ directory. When Claude Code loads your project, it reads these skill files and gains the instructions, workflows, and tool-calling patterns defined in them. No compilation. No build step. You drop a file in a folder and Claude Code gets smarter. The technical implementation is elegant. Each skill defines a trigger (usually a slash command like /seo-audit ), a set of instructions that Claude follows, and optionally some configuration. When you invoke the skill, Claude Code executes the instructions using its existing capabilities: reading files, running shell commands, making API calls, and writing output. The skill just tells it what to do and in what order. ## How to Install a Claude Code Skill Installation is straightforward. Most skills are distributed as GitHub repos. Here's the general process: ``` # Clone the skill repo (or just the skill files) git clone https://github.com/AgriciDaniel/claude-seo.git # Copy the skill files into your project cp -r claude-seo/.claude/skills/seo your-project/.claude/skills/ # That's it. Start Claude Code in your project claude ``` Once the skill files are in your .claude/skills/ directory, Claude Code automatically picks them up. No restart required if you're adding skills while Claude Code is running (it watches for file changes). Some skills also include a CLAUDE.md file with project-level instructions that you can merge into your own. A few skills require external dependencies (API keys, CLI tools, etc.). These are always documented in the skill's README. For example, claude-seo needs Node.js for some of its analysis tools, and claude-ads needs API access to your ad platforms. ## Top Claude Code Skills Ranked by GitHub Stars I'm ranking these by GitHub stars because it's the closest thing we have to a community vote. Stars aren't a perfect metric (plenty of great tools are under-starred), but for a "best of" list, it's the most objective starting point. All star counts are as of March 2026. ### 1. claude-seo - 2,974 Stars Deep dive: [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) The most-starred Claude Code skill in existence, and yes, I built it. (Bias disclosed. Moving on.) claude-seo turns Claude Code into a comprehensive SEO auditing tool. You point it at a URL or a local project and it runs a full technical SEO audit: crawlability, indexing issues, meta tag analysis, heading hierarchy, schema markup validation, Core Web Vitals assessment, internal linking analysis, and content optimization scoring. Key features: - Full technical SEO audit with 50+ checkpoints - Content optimization scoring with specific fix suggestions - Schema markup generation and validation (Article, FAQ, HowTo, Product, and more) - Keyword density and placement analysis - Competitor content gap analysis - Internal linking recommendations - Core Web Vitals diagnostics - Bulk URL processing for site-wide audits The thing that sets it apart from paid tools isn't any single feature - it's that everything runs locally from your terminal with no data limits, no API key walls (for the core features), and no monthly subscription. It's MIT licensed and free forever. ``` # Install git clone https://github.com/AgriciDaniel/claude-seo.git # Use in any project /seo-audit https://example.com ``` [View on GitHub](https://github.com/AgriciDaniel/claude-seo) ### 2. claude-ads - 1,171 Stars Deep dive: [Claude Code Just Replaced Your Ad Agency](/blog/claude-code-ad-agency) claude-ads does for advertising what claude-seo does for search. It audits your ad accounts across Google Ads, Meta Ads, and TikTok Ads, then gives you specific, actionable recommendations. Not vague "optimize your targeting" advice. Specific things like "Campaign X has a 4.2% CTR on mobile but 1.1% on desktop - pause desktop or create device-specific creatives." Key features: - Multi-platform support: Google Ads, Meta Ads, TikTok Ads - Budget waste detection (identifies campaigns burning money with no conversions) - Audience overlap analysis - Creative performance scoring - Bid strategy optimization recommendations - ROAS and CPA benchmarking against industry averages - Export audit reports as structured documents This one requires API credentials for your ad platforms. Setup takes about 10 minutes if you already have API access, longer if you need to create developer accounts. ``` # Install git clone https://github.com/AgriciDaniel/claude-ads.git # Audit a Google Ads account /ads-audit - platform google - account-id 123-456-7890 ``` [View on GitHub](https://github.com/AgriciDaniel/claude-ads) ### 3. claude-blog - 300 Stars Deep dive: [Claude Code Just Replaced Your Blog Writer](/blog/claude-code-blog-writer) claude-blog is a content creation skill that generates blog posts optimized for SEO from the start. You give it a topic and target keyword, and it produces a full article with proper heading hierarchy, keyword placement, meta tags, and schema markup. It's essentially the writing engine from Rankenstein, extracted into a standalone Claude Code skill. Key features: - SEO-optimized article generation from a single keyword - Configurable tone and voice profiles - Automatic heading hierarchy (H1 through H4) - Meta title and description generation - Schema markup output (Article, BlogPosting) - Internal linking suggestions based on your existing content - Readability scoring and adjustment The voice profile system is what makes this one special. You can define your writing style (sentence length, vocabulary level, tone, and specific words to avoid) and claude-blog will match it consistently. I use it for first drafts that I then edit for personality. Saves about 60% of writing time. ``` # Install git clone https://github.com/AgriciDaniel/claude-blog.git # Generate a blog post /blog-write - keyword "n8n automation" - tone professional ``` [View on GitHub](https://github.com/AgriciDaniel/claude-blog) ### 4. banana-claude - 41 Stars Deep dive: [banana-claude: AI Image Generation That's Surprisingly Good at Logos](/blog/banana-claude-ai-image-generation) banana-claude adds image generation capabilities to Claude Code. It integrates with multiple image generation APIs (Stable Diffusion, DALL-E, Midjourney via API) and lets you generate, edit, and batch-process images directly from your terminal. It's particularly useful for generating blog featured images and social media assets without leaving your workflow. Key features: - Multi-provider support (Stable Diffusion, DALL-E, Flux) - Batch generation with variable prompts - Image-to-image editing - Automatic resizing for different platforms (blog, Twitter, LinkedIn, Instagram) - Prompt optimization (rewrites your prompt for better results) ``` # Install git clone https://github.com/AgriciDaniel/banana-claude.git # Generate an image /banana - prompt "minimalist tech blog header, dark background" - size 1200x630 ``` [View on GitHub](https://github.com/AgriciDaniel/banana-claude) ### 5. claude-shorts - 27 Stars claude-shorts automates short-form video editing. You feed it a long video and it identifies the most engaging segments, cuts them, adds captions, and exports them in formats optimized for YouTube Shorts, TikTok, and Instagram Reels. If you're repurposing long-form content into shorts, this skill cuts the editing time from hours to minutes. Key features: - Automatic highlight detection in long-form video - AI-powered caption generation and styling - Aspect ratio conversion (16:9 to 9:16) - Multi-platform export presets - Batch processing for multiple videos Requires FFmpeg installed locally (which you probably already have if you're doing any video work). ``` # Install git clone https://github.com/AgriciDaniel/claude-shorts.git # Process a video /shorts - input video.mp4 - max-clips 5 - captions true ``` [View on GitHub](https://github.com/AgriciDaniel/claude-shorts) ### 6. skill-forge - 23 Stars Deep dive: [skill-forge: Build Your Own Claude Code Skills](/blog/skill-forge-build-claude-code-skills) skill-forge is the meta-tool. It's a Claude Code skill for building Claude Code skills. You describe what you want your skill to do, and skill-forge generates the skill files, directory structure, slash command configuration, and README. It's the fastest way to go from "I wish Claude Code could do X" to a working skill in your project. Key features: - Interactive skill scaffolding - Automatic slash command registration - Best-practice file structure generation - Skill testing and validation - README and documentation generation - GitHub repo template setup ``` # Install git clone https://github.com/AgriciDaniel/skill-forge.git # Create a new skill /forge - name "my-skill" - description "Does something awesome" ``` [View on GitHub](https://github.com/AgriciDaniel/skill-forge) ## How to Build Your Own Claude Code Skill If none of the existing skills do what you need (or if you want to customize one), building your own is surprisingly straightforward. Here's the minimal viable skill: ``` # 1. Create the skill directory mkdir -p your-project/.claude/skills/my-skill # 2. Create the skill file cat > your-project/.claude/skills/my-skill/skill.md ``` That's a working skill. The markdown frontmatter defines the command trigger and metadata. The body contains the instructions Claude Code follows when the skill is invoked. You can make these instructions as simple or complex as you need. For more sophisticated skills, you can: - Split instructions across multiple markdown files - Include example inputs and outputs for few-shot learning - Define configuration options that users can customize - Reference external tools and APIs that Claude Code should call - Chain multiple skills together in a workflow Or just use skill-forge and skip the manual setup entirely. It generates all of this scaffolding from a description of what you want. ## The Ecosystem Is Growing Fast When I published claude-seo in August 2025, there were maybe a dozen Claude Code skills total. As of March 2026, I've counted over 300 on GitHub alone. The growth rate is accelerating because building skills has near-zero friction - if you can write a clear markdown document, you can build a skill. The most active areas of development right now are: - Marketing and SEO (my lane, obviously) - audit tools, content generators, analytics integrations - DevOps and infrastructure - deployment automation, monitoring, incident response - Data analysis - CSV processing, database querying, visualization generation - Content creation - writing, image generation, video editing - Code quality - review automation, testing, documentation generation I expect this list to look completely different in 6 months. The tooling is still young, and the community is experimenting aggressively. ## Frequently Asked Questions ### Do Claude Code skills cost money? The skills themselves are free (most are open-source and MIT licensed). However, Claude Code itself requires an Anthropic API key or a Claude subscription, and some skills make external API calls that may have their own costs. For example, claude-seo's core features are completely free, but if you want DataForSEO keyword data integration, you'll need a DataForSEO API key. The skill always tells you upfront what external dependencies it needs. ### Can I use multiple skills in the same project? Yes. Skills are designed to coexist. You can have claude-seo, claude-blog, and banana-claude all installed in the same project and use them independently. The only potential conflict is if two skills define the same slash command, which is rare and easy to fix by renaming one. ### How do skills compare to MCP (Model Context Protocol) servers? They solve different problems. MCP servers provide Claude Code with access to external data sources and APIs (databases, SaaS tools, file systems). Skills provide Claude Code with specialized workflows and instructions. You'll often use both together. For example, claude-ads uses MCP connections to pull data from your ad platforms, then the skill instructions tell Claude how to analyze that data and generate recommendations. MCP is the plumbing. Skills are the intelligence layer on top. ### What's the best way to stay updated on new skills? The [AI Marketing Hub on Skool](https://www.skool.com/ai-marketing-hub) is where I share new releases first. GitHub's explore page for the "claude-code" topic is another good source. And if you're building skills yourself, the [skill-forge repo](https://github.com/AgriciDaniel/skill-forge) has a community section where people share what they're working on. ## What's Next I'm currently working on two new skills that I'll announce in the next few weeks. One is focused on email marketing automation, and the other is a competitive intelligence tool that goes way deeper than what claude-seo currently offers. If you want early access, join the [AI Marketing Hub](https://www.skool.com/ai-marketing-hub) - Pro members get first access to everything I ship. The Claude Code skill ecosystem is the most exciting thing happening in AI tooling right now. Not because any single skill is revolutionary, but because the barrier to building and sharing specialized AI workflows has dropped to nearly zero. If you can describe what you want in plain English, you can build a tool that does it. That's a big deal. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [skill-forge: Build Your Own Claude Code Skills](/blog/skill-forge-build-claude-code-skills) - Build and publish Claude Code skills in minutes, not hours - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) ## Specialist skills to inspect next See how [claude-cybersecurity audits code with parallel agents](/blog/claude-cybersecurity-ai-security-audit) and how [claude-music builds a local AI music workflow](/blog/claude-music-ai-production). *** # Claude Code Just Replaced Your Ad Agency - 250+ Checks in One Command - URL: [https://agricidaniel.com/blog/claude-code-ad-agency](https://agricidaniel.com/blog/claude-code-ad-agency) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Ad agencies charge $5-10K/month to run audits you can now do in your terminal. claude-ads runs 250+ automated checks across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, and Apple Ads - for free. ## Your Ad Agency Charges $10K/Month to Read Dashboards I'm going to say something that will upset a lot of media buyers: most ad agency "audits" are someone junior logging into your ad accounts, screenshotting some graphs, and pasting them into a deck with their logo on it. You're paying $5,000 to $10,000 per month for a process that can be fully automated. I know because I've been on both sides. I've hired agencies. I've seen the deliverables. And now I've built a tool that does what they do, except it runs 250+ checks instead of the 15-20 a human realistically covers, and it does it in minutes instead of weeks. It's called [claude-ads](https://github.com/AgriciDaniel/claude-ads), it's open source, one of the [top Claude Code skills in 2026](/blog/best-claude-code-skills-2026), and it just hit 42,000 views on the demo video alone. v1.5 Update (April 2026) 250+ audit checks (was 186). 7 platforms (added Apple Ads). 3 new skills: PPC calculator, A/B test designer, PDF report generator. All platform best practices updated with 2025-2026 research. SSRF protection and security hardening. [Read the full v1.5 release notes.](/blog/claude-ads-v1-5-release) ## 250+ Checks Across 7 Platforms When you run /ads audit in Claude Code, 6 parallel agents analyze your ad accounts simultaneously. Here's what gets covered: Platform Coverage Grid 250+ weighted audit checks across 7 advertising platforms Google Ads 80 checks Search PMax AI Max Meta Ads 50 checks Pixel/CAPI Andromeda LinkedIn Ads 27 checks B2B TLA Lead Gen TikTok Ads 28 checks Creative Smart+ Shop Microsoft Ads 24 checks Copilot CTV Import Apple Ads 35+ checks CPPs Max Conv YouTube 15 checks Shorts DemandGen CTV 250+ audit checks distributed across 7 ad platforms ### Google Ads (80 checks) - Conversion tracking validation, Enhanced Conversions, Consent Mode V2 compliance - Search term waste analysis with negative keyword gap detection - Quality Score optimization with keyword-level breakdowns - Smart Bidding strategy audit (ECPC deprecated March 2025, tCPA/tROAS/Maximize recommended) - Performance Max asset density, brand exclusions, campaign-level negatives - AI Max for Search evaluation (14% avg conversion lift) - Demand Gen campaigns (replaced Video Action Campaigns April 2026) - CTV measurement (Floodlight does NOT work on CTV devices) ### Meta Ads (50 checks) - Pixel and Conversions API health with EMQ scoring (target: Purchase 8.5+, AddToCart 6.5+) - Andromeda creative diversity (Similarity Score >60% = retrieval suppression) - Creative fatigue detection (lifespan compressed to 14-21 days under Andromeda) - Audience overlap analysis and Advantage+ Sales structure - Placement performance and cost per result trends - Threads placement evaluation (emerging, ~0.04% of spend) ### YouTube Ads (15 check IDs) - Skippable, Non-Skippable, Bumper, Shorts, and Demand Gen format evaluation - Hook quality analysis (first 5 seconds), ABCD creative framework - CTV measurement strategy (75% of YouTube ad spend now on CTV) - Frequency management and audience targeting ### LinkedIn Ads (27 checks) - Company size, seniority, and ABM targeting efficiency - Thought Leader Ads evaluation (CPC $2.29-4.14 vs $13.23 standard) - Lead gen form completion rates vs industry benchmarks - Manual CPC bidding recommended first (Maximum Delivery is most expensive) - CRM integration with Salesforce/HubSpot for revenue attribution - EU Sponsored Messaging compliance (discontinued Jan 2022) ### TikTok Ads (28 checks) - Spark Ads vs standard creative performance (+30% completion, +142% engagement) - Hook rate analysis and creative lifespan monitoring (7-10 day average) - Smart+ modular control (lock targeting, creative, budget, placement independently) - GMV Max mandatory for TikTok Shop (July 2025) - Search Ads toggle (20% conversion uplift with In-Feed) - Events API Gateway with ttclid passback ### Microsoft Ads (24 checks) - Google Ads import validation and scheduled import safety checks - Copilot ad placement evaluation (+73% CTR vs traditional search) - LinkedIn Profile Targeting (16% greater CTR, 64% greater CVR) - CTV campaigns, Video ads (9:16 vertical April 2025) - Consent Mode deadline (May 5, 2025 for EEA/UK) - CPCs 30-70% cheaper than Google (the underestimated competitor) ### Apple Ads (35+ checks) - Campaign structure (BOFU/MOFU/Search Match), bid health, Custom Product Pages - Maximize Conversions bidding (GA Feb 26, 2026, installs only) - AdAttributionKit dual attribution (April 10, 2025) - 78% of App Store search volume from devices with Personalized Ads off - TAP placement coverage (Today, Search, Product Pages) ## How the Parallel Audit Works When you run /ads audit , 6 specialized agents launch simultaneously. Each handles a specific domain. They run in parallel, not sequentially, so a full audit across all platforms takes minutes. text { font-family: 'Space Grotesk', system-ui, -apple-system, sans-serif; } /ads audit - audit-google 80 audit-meta 50 audit-creative 21+ audit-tracking 8+ audit-budget 24 audit-compliance 18+ Unified Health Score (0–100) 6 parallel agents analyzing your ad accounts simultaneously ## How the Scoring Works Every check gets a severity multiplier (Critical 5.0x, High 3.0x, Medium 1.5x, Low 0.5x) and a category weight. The weighted formula produces a 0-100 health score with an A through F grade. Critical findings dominate the score, which means a single broken conversion tracking setup tanks your grade regardless of how clean everything else is. Weighted Scoring Algorithm text { font-family: 'Space Grotesk', system-ui, sans-serif; } Weighted Scoring Algorithm Score = Σ(Pass × Severity × CategoryWeight) / Σ(Total × Severity × CategoryWeight) × 100 Category Weights 6 categories Conversion Tracking (25%) Wasted Spend (20%) Structure (15%) Keywords (15%) Ads (15%) Settings (10%) Severity Multipliers Critical 5.0x High 3.0x Medium 1.5x Low 0.5x Example Impact Failed Critical check in Conversion Tracking: Impact = 5.0 (severity) × 0.25 (category) = 1.25 pts Weighted scoring algorithm with severity multipliers and category weights ## What the Output Actually Looks Like You don't get a 60-slide deck. You get a prioritized, scored list of issues ranked by estimated revenue impact . Each finding includes: The specific problem (e.g., "Campaign X has 23% search term waste - $4,200/mo in irrelevant clicks") - Severity rating (critical / high / medium / low) with weighted multiplier - Exact fix with step-by-step instructions - Estimated monthly savings or revenue lift - Quick Wins flagged for fixes under 15 minutes The critical findings alone, conversion tracking gaps, budget allocation errors, audience overlap, typically identify 15-30% wasted ad spend . On a $50K/month ad budget, that's $7,500-$15,000 in savings found in one command. New in v1.5: /ads report generates a professional PDF with health score gauge, platform comparison charts, and formatted tables you can hand directly to clients. 250+ point ad audit completing in minutes, not weeks ## 3 New Tools (v1.5) Beyond the audit, v1.5 added three skills that fill gaps no competitor covers: - /ads math : PPC financial calculator with 7 tools (CPA, ROAS, break-even, impression share, budget forecasting, LTV:CAC, MER). Works offline with pasted data from exports. - /ads test : A/B test designer with IF/THEN/BECAUSE hypothesis framework, statistical significance calculator, sample size tables, and platform-specific guides for Meta, Google, LinkedIn, and TikTok. - /ads report : Professional PDF report generator with health score gauge chart, platform comparison bars, pass/fail distribution donut, formatted tables, and a content quality guardrail that validates the report before output. ## Why This Is Better Than a Human Audit I'm not saying humans are useless in advertising. Strategy, creative direction, understanding your customer, that still requires a brain. But the audit part? The part where someone checks 250+ things and reports back? That's a checklist, and machines are better at checklists than humans. Here's the comparison: - Agency audit: 2-4 weeks turnaround, 15-20 checks (realistically), $5-10K, biased toward upselling their own services - claude-ads audit: 3-5 minutes, 250+ checks, $0, no conflict of interest The agency doesn't want to tell you that your campaigns are actually fine and you should cut their retainer. claude-ads has no retainer. It just tells you what's broken and how to fix it. ## The Architecture Three layers: directive (orchestrator with quality gates), orchestration (19 sub-skills routing to the right analysis), and execution (10 agents, 25 reference files, 12 industry templates). Everything loads on-demand. No bloat. text { font-family: 'Space Grotesk', system-ui, -apple-system, sans-serif; } DIRECTIVE LAYER ads/SKILL.md Orchestrator · Routing · Quality Gates - ORCHESTRATION LAYER audit-google 80 checks audit-meta 50 checks audit-creative 21+ checks audit-tracking 8+ checks audit-budget 24 checks audit-compliance 18+ checks EXECUTION LAYER 25 References On-demand knowledge 19 Sub-Skills Specialized analysis 12 Templates Industry-specific 3-layer architecture: directive, orchestration, execution ## The 42K View Video The demo video crossed 42,000 views, which tells me this struck a nerve. People are tired of paying for audits that take weeks and miss obvious issues. The most common comment? "I just found $3K/month in wasted spend in my first run." That's not surprising. Most ad accounts have never had a proper 250+ point audit. They've had a person skim through the account for an hour and write up whatever jumped out. ## How to Run It Two options: Plugin install (recommended): /plugin marketplace add AgriciDaniel/claude-ads /plugin install claude-ads@agricidaniel-claude-ads One-liner: curl -fsSL https://raw.githubusercontent.com/AgriciDaniel/claude-ads/main/install.sh | bash Then run /ads audit for the full multi-platform analysis, or /ads google , /ads meta , etc. for single-platform deep dives. The tool works with data you provide: exports, screenshots, or pasted metrics. For live API access, pair it with MCP servers (Google Ads MCP, Adspirer for Meta, GrowthSpree for LinkedIn). The tool is MIT licensed. No usage limits, no premium tier, no "contact sales for enterprise." All analysis happens locally. No ad account data leaves your machine. ## What This Means for the Industry Look, I'm not trying to put media buyers out of business. The good ones, the ones who actually develop strategy, test creative angles, and understand unit economics, will thrive. But the ones whose entire value prop is "I log into your ad account and tell you what I see"? That job is automated now. The smart agencies will use tools like this themselves to 10x their audit speed and focus on what actually requires human judgment. The rest will keep charging $10K/month for screenshots. Try it yourself - [claude-ads on GitHub](https://github.com/AgriciDaniel/claude-ads) (2,400+ stars) - Read the [full v1.5 release breakdown](/blog/claude-ads-v1-5-release) with platform research findings - See how claude-ads fits into [the full AI marketing automation stack](/blog/ai-marketing-automation-stack) - Read how I built the [SEO equivalent](/blog/claude-code-seo-stack) that replaced my entire SEO tool stack - Learn more [about me](/about) and why I'm open-sourcing these tools ## Related Posts - [claude-ads v1.5: 250+ Ad Audit Checks Across 7 Platforms](/blog/claude-ads-v1-5-release) - The full v1.5 release with platform research findings and 3 new skills - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive guide to top Claude Code skills ranked by GitHub stars Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # Claude Code Just Replaced Your Blog Writer - AI Slop Is Over - URL: [https://agricidaniel.com/blog/claude-code-blog-writer](https://agricidaniel.com/blog/claude-code-blog-writer) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Most AI-written content is garbage that Google penalizes. claude-blog is different: dual-optimized for Google rankings AND AI citations, with 17 commands, 12 templates, and a 100-point scoring system. ## 95% of AI Content Is Slop - And Google Knows It Let's start with the uncomfortable truth. Most AI-generated blog content is terrible. Not terrible in a "the grammar is wrong" way - terrible in a "this reads like a corporate press release written by a committee of interns" way. Google's helpful content system can detect this pattern, and it's actively demoting it. I've watched sites go from 50K monthly visitors to 8K after publishing 200 AI-generated articles that all have the same structure: generic intro, five H2s with surface-level information, and a conclusion that says "In conclusion, [topic] is important." You've read these articles. You've probably bounced from them within 10 seconds. So has everyone else, and Google's engagement metrics noticed. So when I built claude-blog (one of the [best Claude Code skills in 2026](/blog/best-claude-code-skills-2026)), the first design constraint was: the output cannot read like AI wrote it. Not because AI writing is inherently bad, but because the default AI voice - that sanitized, hedge-everything, say-nothing-controversial tone - is what Google penalizes . The solution isn't to avoid AI. It's to use AI differently. ## What Makes claude-blog Different Most AI writing tools work like this: you give it a keyword, it generates 1,500 words, you publish. Maybe you run it through a "humanizer" (which just adds typos and informal language - Google sees through that too). claude-blog takes a fundamentally different approach with dual optimization for both Google search rankings and AI engine citations . Here's what's under the hood: - 17 commands - From topic research to final publish, each step is a distinct operation you control - 12 content templates - How-to guides, listicles, case studies, comparisons, tutorials, opinion pieces, roundups, news analysis, product reviews, ultimate guides, FAQs, and data-driven reports - 5-category 100-point scoring - Every piece is scored on SEO (20pts), readability (20pts), E-E-A-T signals (20pts), GEO optimization (20pts), and engagement potential (20pts) - Answer-first formatting - Every section leads with the answer, then expands. This is how AI search engines select citations. - Automatic FAQ schema - Generates JSON-LD FAQ markup from the content's natural question-answer pairs Blog generation with live 100-point quality scoring ## The E-E-A-T Problem (and How to Actually Solve It) Google's E-E-A-T framework - Experience, Expertise, Authoritativeness, Trustworthiness - is the biggest hurdle for AI content. How do you demonstrate "experience" when a machine wrote it? Most tools ignore this entirely. claude-blog doesn't. Here's what it does: - Sourced statistics - Every claim gets a citation. Not "studies show" but " a 2025 HubSpot report found that 68% of marketers..." with actual links. Google's quality raters are trained to check for unsourced claims. - First-person experience hooks - The tool prompts you for personal anecdotes and weaves them into the content. This is the "Experience" in E-E-A-T that pure AI cannot fabricate. - Author entity signals - Generates author bio schema, links to your social profiles, and creates topical authority clusters across posts. - Original data integration - If you have your own data (analytics, survey results, case study outcomes), the tool structures it into charts, tables, and quotable statistics that other sites want to link to. This is what separates content that ranks from content that exists. Most AI tools produce the latter. claude-blog aims for the former. ## The GEO Layer Nobody Else Has Generative Engine Optimization is the part of claude-blog that I'm most excited about - and the part that no other AI writing tool touches. When someone asks Perplexity or SearchGPT a question, the AI pulls citations from content that is structured for machine comprehension , not just human readability. What does that mean in practice? - Answer-first paragraphs - The first sentence of every section directly answers the implied question. AI search engines love this because it's easy to extract and cite. - Structured data density - FAQ schema, HowTo schema, article schema with dateModified, speakable markup for voice search - Quotable formatting - Key statistics and definitions are formatted as standalone statements that AI engines can pull verbatim - Topical completeness scoring - The tool checks if you've covered all the subtopics that top-ranking content covers (and flags gaps) I've tested this on my own content. Posts optimized with the GEO layer get cited by Perplexity at 3x the rate of posts without it . That's not a vanity metric - AI search citations drive real traffic. ## The 100-Point Scoring System Every piece of content claude-blog produces gets a score out of 100 across five categories: - SEO (20 points) - Keyword placement, title tag optimization, heading hierarchy, internal linking, meta description quality - Readability (20 points) - Flesch score, sentence variety, paragraph length, transition usage, jargon density - E-E-A-T (20 points) - Source citations, author signals, experience markers, topical authority indicators - GEO (20 points) - Answer-first formatting, schema markup, citation-worthiness, structured data completeness - Engagement (20 points) - Hook strength, scroll depth predictions, CTA placement, visual content suggestions The tool won't let you publish anything below 70 without a warning. In my testing, content scoring 85+ consistently outranks content scoring below 70 within 4-6 weeks. The scoring isn't arbitrary - it's calibrated against actual ranking data from 500+ blog posts I've tracked. Dual optimization - ranking for both Google and AI search engines ## The 13K View Demo The demo video hit 13,000 views, and the feedback confirmed what I suspected: people don't want another AI writing tool that produces mid content faster. They want a tool that produces good content that actually ranks. The most common response was some variation of "I've been looking for something that handles the SEO and the writing in one pass." That's exactly what this does. Research, outline, draft, optimize, score, publish - one workflow, one tool. ## How It Fits with the Rest of the Stack If you've read my posts on [claude-seo](/blog/claude-code-seo-stack) and [claude-ads](/blog/claude-code-ad-agency), you can see where this is going. claude-blog handles content creation, claude-seo audits the technical and on-page foundation, and claude-ads drives paid traffic to the content. It's a full marketing stack in your terminal. I detail the entire setup in [my AI marketing automation stack breakdown](/blog/ai-marketing-automation-stack). The three tools share context. claude-blog knows what keywords claude-seo identified as opportunities. claude-seo validates the schema that claude-blog generates. It's not three separate tools duct-taped together - it's one system. ## Try It It's open source, MIT licensed, and free: - Install [Claude Code](https://github.com/AgriciDaniel/claude-blog) - Run /blog write - topic "your topic" - keyword "your focus keyword" - Review the scored output, add your personal experience, and publish The AI slop era of content is ending - not because AI stopped writing, but because the bar for what ranks just got higher. claude-blog raises your content to meet that bar. - Star the repo on [GitHub](https://github.com/AgriciDaniel/claude-blog) - Learn more [about me](/about) and the tools I'm building - See it applied to a real technical deep dive: [DeepSeek Harness, explained and reviewed](/blog/deepseek-harness-plain-english-guide) - Join the [Claude Code community on Skool](https://www.skool.com/claude-code) ## Related Posts - [BBC AI 2027 video disclosure case study](/blog/bbc-ai-2027-video-ai-slop-case-study) - what frame-by-frame evidence says about labelling generated video - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [Claude Code Just Replaced Your Ad Agency](/blog/claude-code-ad-agency) - 186 automated ad audit checks across 6 platforms, for free - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [Claude Blog v2.1.1: What Changed and What to Do Now](/blog/claude-blog-v2-1-1-release) - the latest release notes, including the Google-guidance realignment Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # Claude Code Just Replaced Your Entire SEO Stack - Here's How - URL: [https://agricidaniel.com/blog/claude-code-seo-stack](https://agricidaniel.com/blog/claude-code-seo-stack) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I cancelled $300/month in SEO tools and replaced them with a single terminal command. Here's the full breakdown of claude-seo: 14 sub-skills, 9 parallel agents, and zero monthly fees. ## You're Paying $300/Month for Something a Terminal Command Does Better I used to pay for Ahrefs ($99/mo), Surfer SEO ($89/mo), Screaming Frog ($26/mo), and a handful of other tools that - let's be honest - I only used 30% of. That's $300+ per month to run audits I could automate . (I wrote a full breakdown of [free SEO audit tools that actually work](/blog/free-seo-audit-tools) if you want the comparison.) So I did. I built [claude-seo](https://github.com/AgriciDaniel/claude-seo), an open-source SEO skill for Claude Code that runs a full technical audit, content analysis, schema markup check, Core Web Vitals assessment, and Generative Engine Optimization pass - all from one command in your terminal. No browser tabs. No dashboards. No credit card. And before you say "another AI wrapper" - this isn't a ChatGPT prompt. It's 14 sub-skills orchestrated by 9 parallel agents that actually crawl your site, parse your HTML, and give you ranked, actionable fixes. Let me walk you through it. ## What claude-seo Actually Does When you run /seo in Claude Code pointed at your project, here's what fires off: - Technical Audit Agent - Crawls your sitemap, checks canonical tags, hreflang, robots.txt, redirect chains, and 404s - On-Page Agent - Analyzes title tags, meta descriptions, heading hierarchy, keyword density, and internal linking - Schema Agent - Validates existing structured data and generates missing JSON-LD (Article, FAQ, HowTo, Organization) - Core Web Vitals Agent - Flags render-blocking resources, image optimization opportunities, CLS issues, and LCP bottlenecks - Content Agent - Scores readability, checks for thin content, and suggests content gaps based on SERP analysis - GEO Agent - Optimizes for AI search engines (Perplexity, SearchGPT, Gemini) with answer-first formatting and citation hooks - Backlink Agent - Analyzes your link profile and identifies toxic links - Local SEO Agent - Checks NAP consistency, Google Business Profile optimization, and local schema - Competitor Agent - Pulls top 10 SERP results for your focus keywords and reverse-engineers their strategies All 9 agents run in parallel. On a typical 50-page site, the full audit completes in under 3 minutes . Try getting that from an agency. claude-seo audit running in real-time - 9 agents analyzing a site in parallel ## The Actual Command Here's what it looks like in practice. You open your project in Claude Code and type: ``` /seo audit - url https://yoursite.com - focus-keyword "your target keyword" ``` The output is a prioritized list of fixes, sorted by impact. Not a 47-page PDF that nobody reads - a ranked action list you can execute right there in the terminal . Claude Code can even apply the fixes for you. Found a missing meta description? It writes one. Schema markup missing? It generates the JSON-LD and injects it into your page. H1 tag duplicated across 12 pages? Fixed in seconds. Prioritized audit output - ranked by impact, ready to execute Here's the video walkthrough: ## The $300/Month Comparison Let me be specific about what you're replacing: - Ahrefs ($99/mo) - claude-seo covers site audit, content gap analysis, and backlink checks. You lose the massive backlink database, but for most small-to-mid sites, the audit is what matters. - Surfer SEO ($89/mo) - The on-page agent handles content scoring, keyword density, and NLP optimization. It doesn't have Surfer's real-time SERP correlation data, but it does something Surfer can't: it actually rewrites your content in-place . - Screaming Frog ($26/mo) - The technical audit agent crawls your site and catches the same issues: broken links, redirect chains, missing tags, duplicate content. Output is cleaner too. - Schema Pro ($79/year) - The schema agent generates and validates JSON-LD automatically. No WordPress plugin needed. Total saved: $3,600/year . And that's conservative - I haven't counted the agency retainer you might also be paying for someone to read those tool outputs and send you a Google Doc summary. (That's literally what most SEO agencies do. I've worked with enough of them to know.) ## The GEO Angle Nobody's Talking About Here's what makes claude-seo different from just "another SEO tool but in the terminal." It has a dedicated Generative Engine Optimization agent. Traditional SEO tools optimize for Google's blue links. But 40% of Gen Z now starts searches on AI tools , not Google. Perplexity, SearchGPT, Gemini - they all pull from your content differently than Googlebot. The GEO agent optimizes your content to be cited by AI search engines: answer-first formatting, structured data that LLMs can parse, citation-worthy statistics with sources, and FAQ blocks that match how people ask questions in conversational search. This is where SEO is going in 2026, and no $300/month tool stack covers it. If you want the demand side of that picture, I later pulled the actual YouTube numbers on the GEO keyword in [my automated YouTube keyword research run with Claude](/blog/automate-youtube-keyword-research-claude). ## It's Open Source and MIT Licensed No waitlist. No freemium tier. No "enterprise plan" for the features you actually need. Clone the repo, install Claude Code, and run it. The entire codebase is MIT licensed - fork it, modify it, sell it if you want . If you're running a business and paying for SEO tools, try this for one audit on one site. If the output isn't as good as what you're paying for, go back to your tools. But I think you'll be surprised. - Star the repo on [GitHub](https://github.com/AgriciDaniel/claude-seo) - Join the [Claude Code community on Skool](https://www.skool.com/claude-code) to share your audit results and get help - Read more [about me](/about) and why I'm building these tools The SEO industry has been charging too much for too long for things that should be automated. Now they are. See how claude-seo fits into [the full AI marketing automation stack I use daily](/blog/ai-marketing-automation-stack). ## Related Posts - [Free SEO Audit Tools That Actually Work](/blog/free-seo-audit-tools) - Genuinely free SEO audit tools that replace paid subscriptions - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive guide to top Claude Code skills ranked by GitHub stars - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) ## Go deeper on local SEO For source-cited Map Pack work, read how the [Local SEO Brain](/blog/claude-code-local-seo-brain) turns raw exports into an inspectable operating system. *** # Free SEO Audit Tools That Actually Work (No $300/month Required) - URL: [https://agricidaniel.com/blog/free-seo-audit-tools](https://agricidaniel.com/blog/free-seo-audit-tools) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: SEO - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. Ahrefs costs $99-999/month. Semrush is $120-450/month. Here's what you can do for free with open-source tools and why I stopped paying for most of them. Let me show you the math that radicalized me. In 2025, I was paying $99/month for Ahrefs, $120/month for Semrush (because each one had features the other didn't), and $259/year for Screaming Frog. That's $2,887 per year on SEO tools. I now pay $0 for the same core functionality, and my audit quality actually improved. This post is about how. Before the pitchforks come out: I'm not saying paid tools are worthless. Ahrefs has an incredible backlink index. Semrush's historical data is unmatched. If you're an agency billing clients $5K/month per account, those subscriptions pay for themselves. But if you're a solo builder, a startup, or someone who just needs solid SEO audits without the enterprise price tag, there's a better way. ## The Problem With "Free" SEO Tools Search "free SEO audit tool" and you'll get a page full of results that are technically free in the same way a hotel minibar is technically accessible. You can use them, but you'll pay. Every major SEO platform offers a "free" tier designed to show you just enough data to make you desperate for more. Ahrefs Webmaster Tools gives you limited site audit and backlink data for verified sites only. Semrush's free plan caps you at 10 searches per day with truncated results. Moz's free tools show you domain authority but hide the actionable details. Ubersuggest shows you 3 results and then hits you with the paywall. These aren't free tools. They're product demos with good SEO. The tools I'm going to cover are genuinely free. Open-source, no usage limits, no "upgrade to see more" walls. Some require technical comfort with a terminal. If that's a dealbreaker, this article probably isn't for you. But if you can run a command in your terminal (and in 2026, Claude Code can literally do it for you), keep reading. ## The Open-Source Alternative: claude-seo Full disclosure: I built this. But hear me out, because the numbers speak for themselves. [claude-seo](https://github.com/AgriciDaniel/claude-seo) has 2,974 GitHub stars, making it the most-starred Claude Code skill in existence. That's not marketing. That's almost 3,000 developers independently deciding this tool is worth using. claude-seo is a Claude Code skill that runs a comprehensive SEO audit from your terminal. You give it a URL, it gives you a detailed technical audit with specific fix recommendations. No account required. No API key for core features. No data limits. MIT licensed. Here's what it does compared to the paid alternatives: ### Feature Comparison: claude-seo vs. Paid Tools Feature claude-seo Ahrefs Semrush Screaming Frog Technical SEO Audit 50+ checkpoints 100+ checkpoints 130+ checkpoints 200+ checkpoints Content Analysis Full (AI-powered) Basic Full Basic Schema Validation All major types Limited Yes Yes Keyword Research Via DataForSEO API Full database Full database No Backlink Analysis Basic (via free APIs) Best in class Extensive No Core Web Vitals Yes (via Lighthouse) Yes Yes Yes (paid) AI Recommendations Yes (Claude-powered) Limited Limited No Price $0 $99-999/mo $120-450/mo $259/yr Data Limits None By plan By plan 500 URLs free Open Source MIT License No No No Where does claude-seo fall short? Two areas: backlink analysis and historical data. Ahrefs has spent years building the largest backlink index on the internet. No open-source tool is going to match that overnight. And Semrush's historical SERP tracking is genuinely valuable for long-term SEO strategy. But for the 80% of SEO work that is technical auditing and content optimization, claude-seo matches or exceeds what you get from paid tools. I wrote a [full walkthrough of how claude-seo replaces a $300/month SEO stack](/blog/claude-code-seo-stack). ## Other Genuinely Free Tools Worth Using claude-seo isn't the only free tool in the stack. Here are the others I use daily, all $0. ### Google Search Console This is the most underrated SEO tool in existence and it's completely free. Google literally tells you which queries your site appears for, your click-through rates, your average positions, and which pages have indexing issues. Most people glance at the overview dashboard and ignore the rest. That's a mistake. The Performance report alone is worth more than most paid keyword tools. Filter by page, by query, by date range, by country. Look at queries where you rank positions 5-15 (the "striking distance" keywords). These are pages that need minor optimization to jump to page 1. I've moved dozens of pages from position 8-12 to the top 5 just by analyzing Search Console data and tweaking title tags, meta descriptions, and content depth. The Coverage (now called "Pages") report shows you exactly which of your pages Google can and can't index, and why. Crawl errors, redirect chains, noindex issues - it's all there. Free. ### Google Lighthouse / PageSpeed Insights Lighthouse runs directly in Chrome DevTools (Ctrl+Shift+I, go to the Lighthouse tab) and gives you Core Web Vitals scores, accessibility audit, best practices check, and SEO audit. PageSpeed Insights is the web version that adds real-world CrUX (Chrome User Experience Report) data on top. These tools measure what Google actually uses for ranking signals. Core Web Vitals (LCP, FID/INP, CLS) are confirmed ranking factors. If you're paying for a tool to check your page speed and not also running Lighthouse, you're doing it wrong. Pro tip: run Lighthouse in incognito mode with no extensions. Extensions inject scripts that tank your performance scores and give you inaccurate results. I've seen people panic about a 40 performance score that jumped to 85 once they disabled their ad blocker during the test. ### Rich Results Test Google's [Rich Results Test](https://search.google.com/test/rich-results) validates your structured data markup and shows you exactly which rich results your page is eligible for. FAQ snippets, how-to cards, product listings, recipe cards - if your schema is valid, this tool confirms it. If it's broken, it tells you exactly what's wrong. I use this after every claude-seo audit to double-check the schema markup suggestions. It's the authoritative source because it's Google's own validator. If it says your schema is valid, it's valid. ### Screaming Frog (Free Version) Screaming Frog's free version crawls up to 500 URLs per site. For small to medium sites, that's enough. It gives you a complete technical picture: broken links, redirect chains, duplicate titles, missing meta descriptions, heading hierarchy issues, image alt text audit, and more. The limitation is the 500 URL cap. If your site is larger, you either need the paid version or you use claude-seo (which has no URL limit). For sites under 500 pages, though, the free Screaming Frog crawl combined with claude-seo's AI-powered analysis gives you a more complete audit than any single paid tool. ANNUAL SEO TOOL COSTS Ahrefs $1,188/yr Semrush $1,548/yr Screaming Frog $259/yr claude-seo $0/yr - SAVE $2,995/year with the free stack The annual cost reality - paid tools vs open-source alternatives ## Why Open-Source Wins for SEO Auditing Beyond the obvious price advantage, open-source SEO tools have three structural advantages that paid tools can't match: 1. No data harvesting. When you run an audit through Ahrefs or Semrush, your URL and all the associated data goes to their servers. They aggregate this data (anonymized, sure) to improve their products and databases. When you run claude-seo, the audit runs locally. Your data stays on your machine. For agencies auditing client sites under NDA, this matters a lot. 2. Customizability. If claude-seo's heading hierarchy check doesn't match your team's SEO guidelines, you can modify it. If you want to add a custom check for your specific CMS quirks, you can add it. Try doing that with Ahrefs. Paid tools are take-it-or-leave-it. Open-source tools are take-it-and-make-it-yours. 3. Community-driven improvement. claude-seo has had 47 contributors submit improvements, bug fixes, and new features. The pace of improvement in an active open-source project often outpaces commercial development because the community is larger than any company's SEO team. A user in Germany noticed that schema validation was missing SpeakableSpecification support and added it in a PR. That kind of niche improvement doesn't happen in paid tools until enough enterprise customers complain. ## The Realistic Free Stack Here's the exact stack I'd recommend if you're starting from zero budget: Google Search Console - query performance, indexing status, Core Web Vitals monitoring - claude-seo - technical audits, content optimization, schema generation, AI recommendations - Google Lighthouse - Core Web Vitals deep dive, performance optimization - Rich Results Test - schema validation - Screaming Frog Free - crawl-based technical audit (for sites under 500 pages) This stack covers 80-90% of what you'd get from a $300/month paid tool combination. For the full picture of how these tools connect, see [my complete AI marketing automation stack](/blog/ai-marketing-automation-stack). The missing 10-20% is backlink analysis (use Search Console's link report as a starting point) and historical SERP tracking (use a spreadsheet and check positions manually once a week - it takes 15 minutes). For the backlink gap specifically, you can use Ahrefs' free backlink checker (limited but useful for quick checks) or Google's own link data in Search Console. Neither matches a full Ahrefs subscription, but for most sites, you don't need a full Ahrefs subscription. You need to know who's linking to you and who's linking to your competitors. The free tools get you 70% of the way there. ## Frequently Asked Questions ### Is claude-seo really free? Yes. The core tool is MIT licensed and 100% free. You need Claude Code (which requires an Anthropic API key or Claude subscription), but the skill itself costs nothing. Some optional integrations (like DataForSEO for keyword data) have their own API costs, but the audit functionality is completely free with no limits. ### How does claude-seo compare to Ahrefs Site Audit? Ahrefs Site Audit has more checkpoints (100+ vs 50+) and better crawl infrastructure for very large sites (10,000+ pages). claude-seo has better content analysis (AI-powered vs rule-based), better schema support, and provides more actionable fix recommendations. For sites under 5,000 pages, claude-seo gives you equal or better audit quality. For enterprise sites with millions of pages, Ahrefs' crawl infrastructure is still superior. ### Do I need coding experience to use claude-seo? You need to be comfortable opening a terminal and running a command. That's it. The actual command is /seo-audit https://your-site.com . Claude Code handles everything else. If you can copy and paste a URL, you can run a claude-seo audit. The output is a structured report in plain English, not raw data that requires interpretation. ### Should I cancel my Ahrefs subscription? It depends on what you use it for. If you primarily use Ahrefs for backlink analysis and competitor research, keep it - nothing free matches that. If you primarily use it for site audits and keyword research, try claude-seo + Google Search Console for a month and see if you miss anything. I didn't. Most people are paying $99/month for a tool they use 20% of. Figure out which 20% you actually need and find free alternatives for the rest. ## Get Started If you want to try claude-seo, here's the quick start: ``` # Clone the repo git clone https://github.com/AgriciDaniel/claude-seo.git # Add the skill to your project cp -r claude-seo/.claude/skills/seo your-project/.claude/skills/ # Run an audit claude /seo-audit https://your-site.com ``` If you find it useful, [star the repo on GitHub](https://github.com/AgriciDaniel/claude-seo). It helps other people find the tool, and honestly, watching the star count climb is the main compensation for maintaining an open-source project (the mass adulation is nice too). And if you want to go deeper into SEO automation - building content pipelines, automating audits at scale, connecting everything with n8n - check out the [AI Marketing Hub on Skool](https://www.skool.com/ai-marketing-hub). It's where 220+ Pro members share the SEO workflows and automations that are actually moving the needle. ## Related Posts - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive guide to top Claude Code skills ranked by GitHub stars Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # n8n SEO Automation: Complete Beginner's Guide (2026) - URL: [https://agricidaniel.com/blog/n8n-seo-automation-beginners-guide](https://agricidaniel.com/blog/n8n-seo-automation-beginners-guide) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Automation - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. n8n is the most underrated tool in SEO. Here's how to set it up, 5 workflows you can build today, and how I use it to automate 80% of my SEO work. I've tried every automation tool that exists for SEO. Zapier, Make, custom Python scripts, cron jobs held together with duct tape. n8n is the one I actually use every day, and it's not even close. It's open source, self-hostable, and has a visual workflow builder that makes complex automations feel almost trivial. I run 23 active workflows on a $5/month VPS, handling everything from rank tracking to content publishing to competitor monitoring. This guide will get you from zero to your first working SEO workflow in about 30 minutes. By the end, you'll understand how n8n works, have 5 practical workflow blueprints you can copy, and know how it fits into a broader AI-powered SEO stack. Let's get into it. ## What Is n8n (And Why SEOs Should Care) n8n (pronounced "nodemation") is an open-source workflow automation platform. Think of it as Zapier, but you own the infrastructure, there are no per-task limits, and you can build workflows that would cost $500/month on Zapier for literally $5/month on a cheap VPS. For SEO specifically, n8n is perfect because: - API-native - SEO runs on APIs (DataForSEO, Google Search Console, PageSpeed Insights, Screaming Frog). n8n connects to all of them natively or via HTTP requests. - Scheduling - SEO is repetitive. Rank checks, site audits, content monitoring - these need to run on schedules. n8n handles cron triggers out of the box. - Data transformation - SEO data is messy. n8n has built-in JavaScript/Python nodes for cleaning, transforming, and routing data between systems. - No task limits - Self-hosted means unlimited executions. Track 10,000 keywords daily without worrying about your Zapier bill. - Visual debugging - When a workflow breaks (and they will), you can see exactly which node failed and what data it received. This alone saves hours of debugging compared to code-only approaches. (The name "n8n" stands for "nodemation" which is a portmanteau of "node" and "automation." I know. The naming convention is not its strongest feature.) Here's the stat that convinced me to go all-in: I run approximately 2,000 automated tasks per day across my n8n instance. On Zapier's Professional plan, that would cost $89/month minimum, and I'd probably hit the 2,000 task cap regularly. On n8n self-hosted, it costs me $5/month for the VPS regardless of how many tasks I run. That's an 18x cost difference for the orchestration layer alone. ## Getting Started: Installation in 5 Minutes The fastest way to get n8n running is Docker. If you have Docker installed (and if you're reading this, you probably should), it's one command: ``` docker run -it - rm \ - name n8n \ -p 5678:5678 \ -v n8n_data:/home/node/.n8n \ docker.n8n.io/n8nio/n8n ``` Open localhost:5678 in your browser. That's it. You have a running n8n instance. For production, you'll want to add persistent storage and a reverse proxy, but for learning, this is all you need. For a more permanent setup, I recommend a small VPS (Hetzner CX22 at $4.35/month is what I use) with Docker Compose: ``` version: '3.8' services: n8n: image: docker.n8n.io/n8nio/n8n restart: always ports: - "5678:5678" environment: - N8N_BASIC_AUTH_ACTIVE=true - N8N_BASIC_AUTH_USER=admin - N8N_BASIC_AUTH_PASSWORD=your-secure-password - WEBHOOK_URL=https://your-domain.com/ volumes: - n8n_data:/home/node/.n8n volumes: n8n_data: ``` Set up a Cloudflare tunnel or Caddy reverse proxy, and you've got a production-ready automation server for under $5/month. A few notes on the VPS setup that I learned the hard way: - Give it at least 2GB RAM. n8n itself is light, but when you're running 20+ workflows with data transformations, memory matters. The Hetzner CX22 (2 vCPU, 4GB RAM) is the sweet spot. - Enable swap space. Even with 4GB RAM, complex workflows with large datasets can spike memory usage. A 2GB swap file acts as insurance. - Set up automatic backups. Your workflows are your infrastructure. Hetzner's automated backups cost $0.85/month and save your entire server state nightly. - Use a subdomain like n8n.yourdomain.com instead of an IP address. You'll need it for webhook URLs, and it looks more professional in client-facing integrations. ## Your First Workflow: The Concepts Before we build, let me explain the 4 things you need to understand: - Triggers - What starts the workflow. Can be a schedule (cron), a webhook (external event), or manual. Every workflow starts with exactly one trigger. - Nodes - Each step in the workflow. HTTP requests, data transformations, conditionals, integrations. You drag them onto the canvas and configure them. - Connections - The lines between nodes. Data flows through them. A node can have multiple outputs (like an IF node that branches based on conditions). - Expressions - How you reference data from previous nodes. Uses {{ $json.fieldName }} syntax. This is where most beginners get stuck, but the expression editor has autocomplete that makes it manageable. That's genuinely all there is to it. The visual builder makes the rest intuitive. If you can draw a flowchart, you can build an n8n workflow. One mental model that helped me: think of each node as a function that receives data, does something with it, and passes the result to the next node. The visual canvas is just a way to wire those functions together without writing the boilerplate code that connects them. A typical n8n SEO workflow - trigger, fetch, transform, analyze, store ## 5 Practical SEO Workflows You Can Build Today ### 1. Automated Rank Tracking with DataForSEO This is the first workflow everyone should build. It checks your keyword rankings daily and stores the results in a Google Sheet (or database, if you prefer). How it works: - Cron trigger - Fires daily at 6 AM - Google Sheets node - Reads your keyword list from a spreadsheet - HTTP Request node - Sends each keyword to DataForSEO's SERP API - Code node - Extracts your domain's position from the results - Google Sheets node - Writes the rank + date back to the spreadsheet - IF node - Checks if any keyword dropped more than 5 positions - Slack node - Alerts you about significant drops Cost: about $0.002 per keyword check via DataForSEO. Track 500 keywords daily for $1/day - that's $30/month vs. $65+/month for a dedicated rank tracker. Pro tip: batch your DataForSEO requests. Instead of sending 500 individual API calls, their bulk endpoint lets you send up to 700 keywords in a single request. This is faster, cheaper (bulk pricing), and easier on your n8n server's resources. I batch mine in groups of 100 with a 2-second delay between batches to avoid rate limits. ### 2. Content Performance Monitoring This workflow connects to Google Search Console and monitors your content's search performance over time. How it works: - Cron trigger - Weekly on Monday - HTTP Request - Pulls last 7 days of GSC data (impressions, clicks, CTR, position) for all pages - Code node - Compares current week vs. previous week, calculates deltas - Filter node - Isolates pages with >20% traffic drop - Google Sheets node - Logs the declining pages - Email/Slack node - Sends you a weekly content health report I use a variation of this that also triggers a claude-seo audit on any page that drops more than 30% week-over-week. The audit runs automatically, identifies the probable cause (content decay, new competitor, algorithm shift), and drafts an update plan. I just review and approve. The key insight here is the delta calculation. A page dropping from position 3 to position 5 is very different from a page dropping from position 50 to position 55. My Code node weights the alert severity based on both the absolute position and the magnitude of change. Top-10 drops get immediate Slack alerts. Page-2+ drops get logged for weekly review. ### 3. Competitor Keyword Gap Analysis Know what your competitors rank for that you don't. Updated weekly, automatically. How it works: - Cron trigger - Weekly - HTTP Request - Hits DataForSEO's competitor analysis endpoint for each competitor domain - Code node - Cross-references their keywords with yours, identifies gaps - Filter node - Keeps only keywords with volume >100 and difficulty (The difficulty I track 5 competitors per client site. Each week, the workflow produces a ranked list of keyword opportunities that I'm not targeting but my competitors are. Over 3 months, this workflow identified 127 keyword opportunities that turned into published content, generating an estimated 15,000 additional monthly organic visits across my managed sites. That's the compound value of consistent automation. ### 4. Automated Blog Publishing Pipeline This is where it gets powerful. This workflow takes a keyword, generates a full blog post, optimizes it, and publishes it - with human review in the middle. How it works: - Webhook trigger - Receives a keyword + target URL from your content calendar - HTTP Request - Runs a SERP analysis for the keyword via DataForSEO - Code node - Analyzes top 10 results: word count, headers, topics covered, content gaps - HTTP Request - Sends the content brief to Gemini API for first-draft generation - Code node - Formats the content, adds internal links from your sitemap, optimizes meta tags - Wait node - Pauses for human review (sends draft via Slack/email for approval) - HTTP Request - On approval, publishes to your CMS via API - HTTP Request - Submits the URL to Google for indexing via Indexing API This is essentially what [Rankenstein](/blog/n8n-seo-content-system) does, packaged as a product. Read the full walkthrough of how the advanced workflow operates node by node. The workflow template is available in the AI Marketing Hub for Pro members. The Wait node is critical. I'm a firm believer in human-in-the-loop for content publishing. The automation handles the 80% that's mechanical (research, first draft, formatting, SEO optimization, publishing mechanics). I handle the 20% that requires judgment (tone, accuracy, strategic alignment). This split is what keeps the content quality high while maintaining a publishing cadence that would be impossible manually. ### 5. Site Health Alerting Catch issues before they tank your rankings. How it works: - Cron trigger - Every 6 hours - HTTP Request - Checks your sitemap for new/removed URLs - HTTP Request - Runs PageSpeed Insights on your top 10 pages - HTTP Request - Checks for 4xx/5xx status codes across critical pages - IF nodes - Evaluates thresholds: LCP >2.5s, CLS >0.1, any 5xx errors - Slack/Email node - Fires alerts for anything that fails the checks This has saved me multiple times. Last month it caught a broken redirect chain on a client's highest-traffic page within 6 hours of it happening. Without the alert, that page would have been returning 404s for days before anyone noticed. I also added a node that checks the HTTP response headers for unexpected changes - things like a CDN dropping the cache-control header, or a WAF rule accidentally blocking Googlebot's user agent. These are the subtle issues that don't show up in basic uptime monitoring but can destroy your search performance silently. ## How I Use n8n with claude-seo and Rankenstein The real magic happens when n8n orchestrates the Claude Code skills. Here's the actual flow: - n8n detects an event (new keyword opportunity, ranking drop, content due for update) - n8n triggers claude-seo via CLI or API to analyze the situation - claude-seo returns structured data (audit results, content brief, optimization recommendations) - n8n routes the output to the appropriate next step (content generation, alert, task creation) - Rankenstein handles publishing if the output is content n8n is the orchestrator. claude-seo is the intelligence. Rankenstein is the execution layer. Together, they form a closed loop that can run autonomously - I'm just the quality gate in the middle. The key architectural decision was keeping these layers separate instead of building one monolithic tool. Each layer can be replaced or upgraded independently. If a better orchestration tool than n8n emerges, I can swap it without touching the skills. If I build a better analysis skill, it drops into the same n8n workflows without reconfiguration. This modularity is what makes the whole system maintainable as a solo developer. ## Tips for Scaling ### Error Handling Always add error handling nodes to your workflows. APIs fail, rate limits hit, data comes back malformed. Use n8n's built-in error trigger and set up a "dead letter" workflow that catches and logs all failures. I have one Slack channel that receives every workflow error - it's the first thing I check each morning. On average, I see 2-3 errors per day out of 2,000 tasks. That's a 99.85% success rate, which is good enough for SEO automation where most tasks can simply be retried. ### Scheduling Don't run everything at the same time. Stagger your workflows throughout the day. I run rank tracking at 6 AM, content monitoring at 9 AM, competitor analysis at noon, and site health checks every 6 hours. This spreads the API load and makes debugging easier. It also means I get a steady stream of insights throughout the day instead of everything landing in my inbox at once. ### Webhook Triggers For anything that needs to happen in real-time (new content published, form submission, GSC anomaly), use webhooks instead of cron. n8n's webhook nodes give you a URL that triggers the workflow instantly. Much more responsive than polling. I use webhooks for the content publishing pipeline (triggered from our editorial calendar) and for site health alerts that come from external monitoring services. ### Version Control Export your workflows as JSON and commit them to Git. n8n makes this easy with its export feature. I keep all my workflow JSONs in a private repo and deploy changes via a simple script. Treat your workflows like code, because they are code. I've been burned exactly once by losing a workflow to a server migration. Never again. ### Environment Variables Never hardcode API keys or credentials in your workflows. Use n8n's credential system for built-in integrations, and environment variables for custom HTTP requests. This makes it safe to version-control your workflows (no secrets in Git) and easy to move between development and production instances. ## Common Mistakes I Made (So You Don't Have To) - Not handling pagination. DataForSEO returns paginated results. My first rank tracking workflow only checked the first page of SERP results, which meant any keyword ranked below position 10 showed as "not found." Embarrassing. - Running workflows too frequently. Checking rankings every hour doesn't give you more insight than checking once a day. It just burns through your API budget 24x faster. Match your schedule to the actual rate of change. - Not deduplicating alerts. Without deduplication, the same issue triggers a new Slack message every 6 hours. I now track alert state in a simple Google Sheet - if an issue was already reported and not resolved, it appends to the existing thread instead of creating a new one. - Ignoring timezone issues. My VPS is in UTC. DataForSEO's data is timestamped in UTC. But GSC data has a 2-day lag and uses the property's timezone. My week-over-week comparisons were off by a day until I accounted for this. Small thing, but it made my data unreliable. ## Resources - My YouTube channel (@AgriciDaniel) - I'm building out tutorial content for each of these workflows, with screen recordings of the actual build process - AI Marketing Hub on Skool - The community where people share their n8n + SEO setups. Also check out [the full AI marketing automation stack](/blog/ai-marketing-automation-stack) to see how n8n fits the bigger picture. Free tier available. This is where you'll find people running similar workflows who can help troubleshoot. - Rankenstein templates - Pre-built n8n workflows for SEO, available to Pro members ($88/month). These are production-tested workflows that you import and configure in 20 minutes. - n8n official docs - docs.n8n.io is genuinely excellent documentation. Start with the "Getting Started" guide and the "Building Your First Workflow" tutorial. - DataForSEO API docs - You'll spend a lot of time here. Their docs are thorough, and their support team is responsive if you hit edge cases. - n8n community forum - community.n8n.io has answers to most common questions. Search before asking. ## FAQ ### Do I need to know how to code? Not really. n8n's visual builder handles 90% of what you need. For the other 10% (data transformation, custom logic), basic JavaScript helps. But you can get surprisingly far with just drag-and-drop nodes. If you can write a Google Sheets formula, you can handle n8n expressions. ### How much does DataForSEO cost? Pay-per-use. SERP checks are about $0.002 each. A typical setup tracking 500 keywords daily runs about $30/month. Compare that to Ahrefs at $249/month for similar data. They have a free tier for testing with limited requests, which is enough to build and validate your first workflow. ### Can I use this for client work? Absolutely. I run workflows for multiple client sites from one n8n instance. Each client gets their own set of workflows and data sources. There's no per-seat or per-site pricing - it's your server, run whatever you want on it. Some agency owners in the Hub manage 30+ client sites from a single n8n instance. ### What about n8n Cloud vs. self-hosted? n8n Cloud starts at $24/month and is fine for getting started. But self-hosted gives you unlimited executions, full control, and costs $5/month on a VPS. For SEO automation where you might run thousands of tasks daily, self-hosted pays for itself immediately. My recommendation: start with n8n Cloud to learn, then migrate to self-hosted once you have 5+ workflows running. ### Is this better than Python scripts? For most SEO workflows, yes. n8n gives you visual debugging, built-in scheduling, error handling, and integrations without writing boilerplate. I still use Python for heavy data analysis, but for workflow orchestration, n8n wins every time. The visual nature also makes it easier to hand off workflows to team members who aren't developers. ### How do I get started with Rankenstein? Rankenstein v7 and v8 are available on Gumroad. They're n8n workflow packages that you import into your instance. The setup guide walks you through configuration in about 20 minutes. If you want hands-on help, the Pro tier in the AI Marketing Hub includes setup support and screen-share onboarding sessions. ### What if n8n goes down? This is why I run it on a VPS with Docker restart policies and automated backups. In 4 months of daily use, I've had exactly 2 unplanned downtimes - both caused by VPS provider maintenance, not n8n itself. Each lasted under 30 minutes. For SEO automation where most tasks are daily, a 30-minute outage is a non-event. But if uptime is critical for your use case, consider running a secondary instance or using n8n Cloud as a backup. ## Related Posts - [How I Built an AI SEO Content System in n8n](/blog/n8n-seo-content-system) - Full walkthrough of the Rankenstein n8n workflow, node by node - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # How I Built an AI SEO Content System in n8n (Full Walkthrough) - URL: [https://agricidaniel.com/blog/n8n-seo-content-system](https://agricidaniel.com/blog/n8n-seo-content-system) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Automation - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I built an end-to-end SEO content pipeline in n8n that turns a single keyword into a fully optimized, published article. Here's the full walkthrough of how Rankenstein works, node by node. Six months ago, publishing a single SEO-optimized blog post took me somewhere between 4 and 8 hours. Topic research, competitive analysis, outlining, writing, optimization passes, schema markup, internal linking, and finally hitting publish. Today that entire pipeline runs in under 12 minutes. The difference is an n8n workflow I co-built with Benjamin Samar called Rankenstein, and in this post I'm going to walk you through every node. ## Why n8n (and Not Zapier or Make) I evaluated all three platforms before committing. Zapier charges per task and gets expensive fast when you're running multi-step AI workflows. Make is solid but the visual builder gets unwieldy past 20 nodes. n8n won because it's self-hostable, has native AI agent nodes, and doesn't charge per execution. When you're running a workflow that hits 6 different APIs per article, that pricing model matters. A lot. We self-host our n8n instance on a $12/month VPS. (New to n8n? Start with my [n8n SEO automation beginner's guide](/blog/n8n-seo-automation-beginners-guide).) Total monthly cost for the entire content operation (including API calls to DataForSEO, Claude, and various SEO tools) comes to about $40-60 depending on volume. Compare that to hiring a content team or paying for enterprise SEO tools. The math is not subtle. ## The Architecture: 6 Stages, 1 Workflow Rankenstein breaks down into six sequential stages. Each one feeds the next, and the whole thing is triggered by a single input: your target keyword. Here's what happens under the hood. The 6-stage Rankenstein pipeline - from keyword research to published, ranked content ### Stage 1: Topic Research & Keyword Intelligence The workflow starts by hitting the DataForSEO API with your seed keyword. It pulls back search volume, keyword difficulty, CPC data, and (this is the important part) related keywords and questions that real people are searching for. We're not guessing what to write about. We're letting actual search data tell us. The node processes the response and builds a keyword cluster: primary keyword, 5-8 secondary keywords, and a list of questions from People Also Ask. This becomes the content brief that drives everything downstream. ### Stage 2: Competitive Analysis Next, the workflow scrapes the top 10 SERP results for your target keyword. For each result, it extracts the title, meta description, heading structure, word count, and content themes. The goal is to understand what Google is already rewarding for this query. An AI node (Claude, via API) analyzes the competitive landscape and identifies content gaps. What are the top results missing? What angles aren't being covered? What questions aren't being answered? This gap analysis is what separates Rankenstein output from generic AI content. You're not just writing another version of what already exists. You're writing the version that fills the holes. ### Stage 3: Outline Generation Using the keyword cluster and competitive analysis, an AI node generates a detailed content outline. We're talking H2s, H3s, suggested word counts per section, internal linking opportunities, and recommended schema types. The outline is the blueprint, and it's built entirely from data - not vibes. We went through probably 15 iterations of the outline prompt before we got it right. The early versions produced outlines that were technically correct but editorially flat. The current version weights user intent heavily and front-loads the most valuable information (because bounce rates don't care about your dramatic buildup). ### Stage 4: Content Writing This is where the heavy lifting happens. The writing node takes the outline, keyword data, and competitive gaps, then generates the full article. We use Claude's API here because (hot take) it produces the most natural long-form content of any model I've tested. GPT-4o is close, but Claude handles nuance and voice better at the 2,000+ word mark. The prompt engineering for this stage alone took 3 weeks to dial in. We inject the keyword cluster, specify exact density targets, enforce heading hierarchy, and include instructions for natural keyword placement. The output reads like a human wrote it because the instructions are detailed enough to prevent the usual AI tells (no "delve," no "landscape," no "it's worth noting"). ### Stage 5: SEO Optimization This is where [claude-seo](/blog/claude-code-seo-stack) enters the pipeline. The raw article gets passed through an optimization node that checks and fixes: title tag and meta description (with keyword placement), heading hierarchy and keyword distribution, internal and external link suggestions, schema markup generation (Article, FAQ, HowTo depending on content type), and readability scoring with specific fixes for sentences that are too long or paragraphs that are too dense. The optimization pass typically improves the on-page SEO score by 15-25 points (measured by our internal scoring system). It's the difference between content that exists and content that ranks. ### Stage 6: Publishing The final stage pushes the optimized article to your CMS via API. We support WordPress (via REST API), Ghost, and custom endpoints. The node handles featured image selection (from a pre-configured library or AI-generated), category assignment, tag application, and scheduling. One keyword in, one published article out. That's the entire interaction. ## The Results (Because That's What Matters) Since launching Rankenstein in December 2025, Benjamin and I have processed over 2,000 articles through the system. Here are the numbers that matter: - Average time per article: 11.4 minutes (down from 4-8 hours manual) - Average on-page SEO score: 87/100 (before manual review) - First-page ranking rate: 34% of articles reach page 1 within 60 days - Cost per article: ~$0.30 in API costs The 34% first-page rate is the number I'm most proud of. Industry average for "optimized" content is somewhere around 5-10%. We're 3-7x that, and the gap widens when you factor in that our articles require almost zero manual editing. ## What I'd Do Differently If I were starting from scratch today, I'd build the competitive analysis stage to cache results. Right now, if you run 10 articles targeting related keywords, it re-scrapes the SERPs each time even though there's significant overlap. That's wasted API calls and wasted time. We're fixing this in v9. I'd also invest in better image generation earlier. The text pipeline was so compelling that we neglected visuals for the first few versions. Content with custom images consistently outperforms content with stock photos (about 23% higher engagement in our testing). ## Try It Yourself Rankenstein is available at [Rankenstein.pro](https://rankenstein.pro). We offer the full n8n workflow template plus setup documentation. If you want to see it in action before committing, the video walkthrough above covers every node in detail. And if you want to see how Rankenstein fits into [the full AI marketing automation stack](/blog/ai-marketing-automation-stack), or connect with other people building AI-powered SEO systems, join the [AI Marketing Hub on Skool](https://www.skool.com/ai-marketing-hub). We've got 158 paid members sharing workflows, results, and optimizations daily. The best n8n SEO automation ideas come from the community, not from me sitting alone tweaking prompts. ## Related Posts - [n8n SEO Automation: Complete Beginner's Guide](/blog/n8n-seo-automation-beginners-guide) - Get started with n8n for SEO with 5 practical workflows - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month - [AI Agent Approval Workflow: A Practical Review Matrix](/blog/ai-agent-approval-workflow) - Define what an agent may prepare, propose, apply, or escalate before it changes a system - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub) *** # skill-forge: Build Your Own Claude Code Skills From Scratch - URL: [https://agricidaniel.com/blog/skill-forge-build-claude-code-skills](https://agricidaniel.com/blog/skill-forge-build-claude-code-skills) - Published: 2026-03-25 - Updated: 2026-03-25 - Category: Claude Code - Status: Historical publication snapshot. Use Current Facts and Authoritative Sources for present-day counts, prices, versions, and capabilities. I got tired of copy-pasting skill structures by hand. So I built skill-forge - a meta-tool that scaffolds, builds, and publishes Claude Code skills in minutes. Here's something that happens when you build a lot of Claude Code skills: you start noticing that 70% of the work is structural. The directory layout, the manifest files, the hook configurations, the README boilerplate - it's all the same across every skill. The actual logic is maybe 30% of the effort. skill-forge exists because I was copy-pasting folder structures between repos like it was 2003. There had to be a better way. So I built one. ## What Is skill-forge? skill-forge is a Claude Code skill that builds other Claude Code skills. Yes, it's recursive. Yes, I find that amusing. More concretely, it's a guided workflow that takes you from "I have an idea for a skill" to "I have a working, publishable skill" in 5-15 minutes depending on complexity. It handles the scaffolding, the structure, the configuration, and the boilerplate. You focus on the logic - the thing that actually matters. It runs as a slash command: /skill-forge . From there, it walks you through a structured design process, generates the skill files, and optionally publishes it to your GitHub. ## Why I Built It After building claude-seo, claude-ads, and claude-blog - each one a fairly complex multi-file skill - I realized I was spending more time on scaffolding than on logic. Every skill needs: - A specific directory structure Claude Code expects - A manifest with the right schema - Slash command definitions with proper argument parsing - Hook configurations for lifecycle events - A README that explains installation and usage - Error handling patterns that work within Claude's execution model The first time I built a skill, it took me 3 hours to get the structure right. The second time, 2 hours. By the fourth time, I automated it. skill-forge is that automation, packaged so anyone can use it. There's a less obvious reason too: consistency. When you build skills by hand, every one ends up slightly different. Different directory naming, different manifest conventions, different error handling patterns. That inconsistency compounds when you're maintaining 21 repos. Bug fixes in one skill's error handling don't transfer to others because the patterns are all bespoke. skill-forge enforces a standard structure, which means maintenance across the entire portfolio becomes predictable. ## How It Works: The 5-Step Process ### Step 1: Design skill-forge asks you what your skill should do, who it's for, and what commands it needs. It uses this to generate a design document - think of it as a blueprint. You review and refine before any code gets written. This step takes about 2 minutes and prevents the most common failure mode: starting to code before you've thought through the command interface. ### Step 2: Scaffold Based on the design, skill-forge creates the entire file structure. Directory layout, manifest, command stubs, configuration files. Everything is in the right place with the right naming conventions. The scaffold includes placeholder comments that explain what each section does, so even if you're new to Claude Code skills, you can navigate the generated code without documentation. skill-forge scaffolds the complete skill structure in seconds ### Step 3: Build This is where you (and Claude) write the actual logic. skill-forge provides the skeleton; you fill in the muscle. It generates starter code for each command based on your design, so you're not starting from a blank file. For API integration skills, it also generates the HTTP client boilerplate with proper error handling, retry logic, and response parsing. ### Step 4: Review skill-forge validates the skill structure, checks for common mistakes (missing manifest fields, incorrect hook syntax, orphaned files), and runs a dry-run test. It catches the errors that would otherwise bite you when you try to install the skill. I've seen people spend hours debugging a skill that won't load, only to find a missing comma in the manifest. The review step catches that in seconds. ### Step 5: Publish Creates a Git repo (or adds to an existing one), writes a proper README with installation instructions, and optionally pushes to GitHub. Your skill is ready for other people to install and use. The generated README includes a badge section, feature list, command reference, and configuration docs - all populated from your design document. No more writing documentation from scratch for every project. ## The 4 Complexity Tiers Not every skill is a 2,974-star monolith like claude-seo. skill-forge supports 4 tiers of complexity: - Single-file skill - One command, one file, simple logic. Think: a quick utility like a URL validator or a text formatter. Built in under 5 minutes. These are the skills you build when you think "I wish there was a slash command for this." Most developers have 10-20 of these personal utilities. - Multi-command skill - Multiple slash commands, shared utilities, some configuration. Think: a content tool with /draft , /optimize , and /publish commands. Built in 10-15 minutes. This is the tier where most practical skills live. - Integration skill - Connects to external APIs, handles auth, manages state. Think: claude-ads with Google Ads API integration. More involved, but skill-forge handles the API client scaffolding, token refresh flows, and rate limit handling. The generated code includes retry logic with exponential backoff - the kind of thing you always mean to implement but never get around to. - Multi-skill orchestration - A skill that coordinates other skills. Think: a project manager skill that triggers claude-seo, claude-blog, and claude-ads in sequence. This is the tier where skill-forge saves the most time because the inter-skill communication patterns are tricky to get right manually. Getting the data flow between skills correct, handling failures gracefully, and maintaining state across skill boundaries requires careful architecture that skill-forge provides as a template. ## Example: Building an Audit Skill in 5 Minutes Let me show you how fast this is. Say you want a skill that audits a website's meta tags. You run /skill-forge and answer the prompts: - Name: meta-audit - Description: Audits meta tags across a website - Commands: /meta-audit (takes a URL, checks title, description, OG tags, canonical) - Tier: Single-file - External APIs: None (uses fetch) skill-forge generates the entire skill. The directory, the manifest, the command with argument parsing, the fetch logic, the output formatting. You review the generated code, tweak the validation rules to your liking, and you have a working skill. Install it with the standard Claude Code skill install flow, and /meta-audit https://example.com works immediately. Here's what the generated skill actually checks out of the box: title tag presence and length (50-60 chars), meta description presence and length (150-160 chars), Open Graph tags (og:title, og:description, og:image, og:url), Twitter Card tags, canonical URL, robots directives, and viewport meta tag. That's a solid foundation. You can add custom checks (like hreflang validation or structured data presence) by editing the generated checker functions. (The generated code is not perfect - it's a starting point. But it's a starting point that already handles edge cases, errors, and output formatting. That's the part that takes forever to write from scratch.) ## How My Other Skills Were Built Every skill I've published since building skill-forge was scaffolded with it: - claude-blog (300 stars) - Multi-command skill, Tier 2. skill-forge generated the /blog command structure, the content pipeline stages, and the CMS integration scaffolding. Total build time from scaffold to first working version: about 4 hours. Without skill-forge, based on my experience with claude-seo, it would have been 12+. - claude-ads (1,171 stars) - Integration skill, Tier 3. skill-forge generated the Google Ads API client, the auth flow, and the campaign management commands. The OAuth2 flow alone would have taken a day to implement from scratch. skill-forge gave me a working auth flow in the scaffold. - banana-claude (41 stars) - Integration skill, Tier 3. skill-forge generated the Gemini API integration and the Creative Director prompt architecture. The multi-step generation pipeline (intent analysis, style mapping, prompt engineering, quality check) was structured from the design phase, which saved a lot of architectural rework. claude-seo predates skill-forge (it was the project that made me realize I needed it), but I've since migrated its structure to match skill-forge conventions. Having a consistent structure across all 21 repos makes maintenance dramatically easier. When I fix a bug in one skill's error handling pattern, I can apply the same fix across all skills mechanically. ## Try It skill-forge is open source, MIT licensed, and sitting at 23 stars on GitHub (small but growing). If you're building Claude Code skills - or thinking about it - it'll save you hours of structural work. Install it, run /skill-forge , and see how fast you can go from idea to working skill. If you build something cool with it, I genuinely want to see it - drop it in the AI Marketing Hub or tag me on GitHub. The Claude Code skill ecosystem is still early (see my ranking of the [best Claude Code skills in 2026](/blog/best-claude-code-skills-2026) for the current landscape). The people building skills now are shaping what the platform looks like for everyone who comes after. skill-forge is my attempt to lower the barrier to entry so more people build more things. The best time to start building skills was 6 months ago. The second best time is today, and now you have a tool that makes it 5x faster. ## Related Posts - [Best Claude Code Skills in 2026](/blog/best-claude-code-skills-2026) - The definitive guide to top Claude Code skills ranked by GitHub stars - [Claude Code Just Replaced Your Entire SEO Stack](/blog/claude-code-seo-stack) - How I replaced $300/month in SEO tools with one terminal command - [AI Marketing Automation: The Open-Source Stack I Use Daily](/blog/ai-marketing-automation-stack) - The full open-source AI marketing stack at $50/month Join 4,500+ AI Marketing Builders Get workflow templates, automation blueprints, and connect with SEOs, agency owners, and creators who ship. [JOIN FREE →](https://www.skool.com/ai-marketing-hub)