[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C2M7RC0WJTCNGKK0THQ8SC":3,"skill-same:01M6C2M7RC0WJTCNGKK0THQ8SC":106},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":16,"sourceHtmlUrl":17,"categories":18,"keywords":32,"publishedAt":60,"linkOnly":61,"markdownContent":62,"markdownBody":63,"frontMatter":64,"refName":71,"commitSha":72,"skillDirectory":73,"forksCount":14,"repositoryHtmlUrl":74,"repositoryHomepageUrl":6,"ownerHtmlUrl":75,"ownerBlogUrl":6,"ownerDescription":76,"updatedAt":77,"install":78,"resources":90},"01M6C2M7RC0WJTCNGKK0THQ8SC","seo-geo",null,"Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says \"AI Overviews\", \"SGE\", \"GEO\", \"AI search\", \"LLM optimization\", \"Perplexity\", \"AI citations\", \"ChatGPT search\", or \"AI visibility\".","noirelab\u002Flinux-setup","noirelab","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142925921?v=4","01M6C29R3FEDGFAXAT0KXY28QX","Kaíque M. Lima","01M6C29R811WX04B0GYHTH1XV6",0,"MIT","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fseo-geo\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fseo-geo\u002FSKILL.md",[19,24,27],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":28,"categoryName":29,"taxonomyType":30,"taxonomyTypeDesc":31},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59],"geo","seo","seo geo","engine","when","generative","for","URL","platform","signals","scoring","optimize","use","user","brand","llms","web search","analysis","perplexity","Citations","Optimization","compliance","content","search","chatgpt","accessibility","web","2026-11-10 10:12:30",false,"---\nname: seo-geo\ndescription: >\n  Optimize content for AI Overviews (formerly SGE), ChatGPT web search,\n  Perplexity, and other AI-powered search experiences. Generative Engine\n  Optimization (GEO) analysis including brand mention signals, AI crawler\n  accessibility, llms.txt compliance, passage-level citability scoring, and\n  platform-specific optimization. Use when user says \"AI Overviews\", \"SGE\",\n  \"GEO\", \"AI search\", \"LLM optimization\", \"Perplexity\", \"AI citations\",\n  \"ChatGPT search\", or \"AI visibility\".\nuser-invocable: true\nargument-hint: \"[url]\"\nlicense: MIT\nmetadata:\n  author: AgriciDaniel\n  version: \"2.2.4\"\n  category: seo\n---\n\n# AI Search \u002F GEO Optimization (May 2026)\n\n## Primary Source: Google's AI Optimization Guide\n\nGoogle's official position, published under Search Central docs:\n\n> \"Optimizing for generative AI search is **still SEO** from Google's\n> perspective. AEO and GEO are rebranded labels for the same work.\"\n\nRead `references\u002Fgoogle-ai-optimization-guide.md` for the full synthesis,\nmyth-busting list (`llms.txt`, chunking, AI-rephrasing, mention-farming,\nall rejected by Google as ineffective), and the Who\u002FHow\u002FWhy test for\ncontent quality.\n\nAudits should frame GEO findings as **SEO fundamentals applied to AI-search\nsurfaces**, not as a separate optimization discipline. When community\nrecommendations contradict Google's primary source, defer to Google and note\nthe contradiction in the report.\n\n## Key Statistics\n\n| Metric | Value | Source |\n|--------|-------|--------|\n| AI Overviews reach | 2.5 billion+ monthly active users, reported from Google I\u002FO 2026 keynote coverage; not confirmed on a Google-owned source; 200+ countries | Third-party I\u002FO reporting |\n| AI Overviews query coverage | ~50% of queries (third-party measurement; varies by country) | Industry data |\n| AI Mode monthly users | 1B+, reported from Google I\u002FO 2026 keynote coverage; not confirmed on a Google-owned source | Third-party I\u002FO reporting |\n| AI Mode model | custom version of Gemini 2.5 | Google |\n| AI-referred sessions growth | 527% (Jan-May 2025) | SparkToro |\n| ChatGPT weekly active users | 900 million | OpenAI |\n| Perplexity monthly queries | 500+ million | Perplexity |\n\n## Critical Insight: Brand Mentions > Backlinks\n\n**Brand mentions correlate 3x more strongly with AI visibility than backlinks.**\n(Ahrefs December 2025 study of 75,000 brands)\n\n| Signal | Correlation with AI Citations |\n|--------|------------------------------|\n| YouTube mentions | ~0.737 (strongest) |\n| Reddit mentions | High |\n| Wikipedia presence | High |\n| LinkedIn presence | Moderate |\n| Domain Rating (backlinks) | ~0.266 (weak) |\n\n**Only 11% of domains** are cited by both ChatGPT and Google AI Overviews for the same query, so platform-specific optimization is essential.\n\n---\n\n## GEO Analysis Criteria (Updated)\n\n### 1. Citability Score (25%)\n\n**Optimal passage length: 134-167 words** for AI citation. And **~44% of AI\ncitations come from the first 30% of a page** (SE Ranking study), front-load\nyour most citable, self-contained answer rather than burying it below the fold.\n\n**Strong signals:**\n- Clear, quotable sentences with specific facts\u002Fstatistics\n- Self-contained answer blocks (can be extracted without context)\n- Direct answer in first 40-60 words of section\n- Claims attributed with specific sources\n- Definitions following \"X is...\" or \"X refers to...\" patterns\n- Unique data points not found elsewhere\n\n**Weak signals:**\n- Vague, general statements\n- Opinion without evidence\n- Buried conclusions\n- No specific data points\n\n### 2. Structural Readability (20%)\n\n**92% of AI Overview citations come from top-10 ranking pages**, but 47% come from pages ranking below position 5, demonstrating different selection logic.\n\n**Strong signals:**\n- Clean H1->H2->H3 heading hierarchy\n- Question-based headings (matches query patterns)\n- Short paragraphs (2-4 sentences)\n- Tables for comparative data\n- Ordered\u002Funordered lists for step-by-step or multi-item content\n- FAQ sections with clear Q&A format\n\n**Weak signals:**\n- Wall of text with no structure\n- Inconsistent heading hierarchy\n- No lists or tables\n- Information buried in paragraphs\n\n### 3. Multi-Modal Content (15%)\n\nContent with multi-modal elements sees **156% higher selection rates**.\n\n**Check for:**\n- Text + relevant images\n- Video content (embedded or linked)\n- Infographics and charts\n- Interactive elements (calculators, tools)\n- Structured data supporting media\n\n### 4. Authority & Brand Signals (20%)\n\n**Strong signals:**\n- Author byline with credentials\n- Publication date and last-updated date\n- **Recency**, content under 3 months old is ~3x more likely to be cited in AI answers; pages left stale 6+ months lose citation eligibility (SE Ranking, 1.3M-citation study). A scheduled refresh program is one of the highest-leverage GEO plays.\n- Citations to primary sources (studies, official docs, data)\n- Organization credentials and affiliations\n- Expert quotes with attribution\n- Entity presence in Wikipedia, Wikidata\n- Mentions on Reddit, YouTube, LinkedIn\n\n**Weak signals:**\n- Anonymous authorship\n- No dates\n- No sources cited\n- No brand presence across platforms\n\n### 5. Technical Accessibility (20%)\n\n**AI crawlers do NOT execute JavaScript.** Server-side rendering is critical.\n\n**Check for:**\n- Server-side rendering (SSR) vs client-only content\n- AI crawler access in robots.txt\n- llms.txt file presence and configuration\n- RSL 1.0 licensing terms\n\n---\n\n## AI Crawler Detection\n\nCheck `robots.txt` for these AI crawlers:\n\n| Crawler | Owner | Purpose | Obeys robots.txt? |\n|---------|-------|---------|---|\n| GPTBot | OpenAI | ChatGPT web search | yes |\n| OAI-SearchBot | OpenAI | OpenAI search features | yes |\n| ChatGPT-User | OpenAI | ChatGPT browsing (user-triggered) | no (user-triggered) |\n| ClaudeBot | Anthropic | Claude web features | yes |\n| PerplexityBot | Perplexity | Perplexity AI search | yes |\n| CCBot | Common Crawl | Training data (often blocked) | yes |\n| anthropic-ai | Anthropic | Claude training | yes |\n| Bytespider | ByteDance | TikTok\u002FDouyin AI | yes |\n| cohere-ai | Cohere | Cohere models | yes |\n| Google-Extended | Google | Gemini\u002FVertex training & grounding opt-out | yes |\n| Google-CloudVertexBot | Google | Site-owner-requested Vertex AI Agent crawls | yes |\n| Google-Agent | Google | Agentic browsing (Project Mariner), acts for a user | **no (user-triggered)** |\n| Google-NotebookLM | Google | Fetches individual user-added source URLs | **no (user-triggered)** |\n| Google Messages | Google | User-triggered fetch | **no (user-triggered)** |\n\n**Recommendation:** Allow GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot for AI search visibility. Block CCBot and training crawlers if desired.\n\n> **User-triggered fetchers ignore robots.txt by design** (Google-Agent, Google-NotebookLM, Google Messages, ChatGPT-User). robots.txt cannot block them, use server-side access controls. Google's canonical crawling\u002Frobots reference moved to **developers.google.com\u002Fcrawling** (migrated 2025-11-20); IP-range files now live at `\u002Fcrawling\u002Fipranges\u002F` and `googlebot.json` was renamed `common-crawlers.json`. Emerging: **Web Bot Auth** (RFC 9421) lets bots authenticate via a `Signature-Agent` header + key directory (used by Google-Agent); reverse-DNS verification remains the fallback.\n\n---\n\n## llms.txt Standard\n\nRead `references\u002Fllmstxt-evidence.md` for the primary-source evidence (Mueller, Illyes, SE Ranking 300k-domain study, OtterlyAI server-log audit) on why `\u002Fllms.txt` is not currently a citation lever for major AI search systems. claude-seo reports presence but assigns no citation-ranking weight.\n\n> **Google now states this explicitly.** Google's AI optimization guide (updated 2026-06-29) says you do **not** need `llms.txt` \u002F AI-text files for Google Search, including its generative AI features, and that doing so \"won't harm (nor help) your visibility or rankings in Google Search, as Google Search ignores them.\" Mueller separately called the llms.txt discovery use case \"a dead end.\" It's fine to keep for **non-Google** AI services; never recommend it as a Google ranking\u002Fcitation lever. Source: developers.google.com\u002Fsearch\u002Fdocs\u002Ffundamentals\u002Fai-optimization-guide\n\nThe emerging **llms.txt** standard provides AI crawlers with structured content guidance.\n\n**Location:** `\u002Fllms.txt` (root of domain)\n\n**Format:**\n```\n# Title of site\n> Brief description\n\n## Main sections\n- [Page title](url): Description\n- [Another page](url): Description\n\n## Optional: Key facts\n- Fact 1\n- Fact 2\n```\n\n**Check for:**\n- Presence of `\u002Fllms.txt`\n- Structured content guidance\n- Key page highlights\n- Contact\u002Fauthority information\n\n---\n\n## RSL 1.0 (Really Simple Licensing)\n\nNew standard (December 2025) for machine-readable AI licensing terms.\n\n**Backed by:** Reddit, Yahoo, Medium, Quora, Cloudflare, Akamai, Creative Commons\n\n**Check for:** RSL implementation and appropriate licensing terms.\n\n---\n\n## Platform-Specific Optimization\n\n| Platform | Key Citation Sources | Optimization Focus |\n|----------|---------------------|-------------------|\n| **Google AI Overviews** | Strongly ranking-correlated, cites pages that already rank well | Traditional SEO + passage optimization |\n| **Google AI Mode** (custom version of Gemini 2.5) | Weakly ranking-correlated; broader pool (~9 domains cited\u002Fquery, Ahrefs) | Distinct surface: freshness, entity authority, citable passages beyond position 5 |\n| **ChatGPT** | Wikipedia (47.9%), Reddit (11.3%) | Entity presence, authoritative sources |\n| **Perplexity** | Reddit (46.7%), Wikipedia | Community validation, discussions |\n| **Bing Copilot** | Bing index, authoritative sites | Bing SEO, IndexNow |\n\n> **Two Google citation engines, not one.** AI Mode and AI Overviews reach the\n> same conclusion ~86% of the time but cite the same URLs only **13.7%** of the\n> time (Ahrefs study, 540K query pairs). Treat them as separate surfaces: ranking\n> well in classic Search feeds AI Overviews, but AI Mode draws from a broader pool\n> where freshness and entity authority outweigh raw position. Score both.\n>\n> **UX is now unified, surfaces still distinct.** At Google I\u002FO 2026 (2026-05-19)\n> Google merged AI Overviews and AI Mode into \"one seamless AI Search experience\"\n> (question → AI Overview → follow-up in AI Mode) with a new intelligent Search\n> box. The *experience* is one flow, but the two citation engines remain\n> technically distinct (different models\u002Flink sets), keep scoring both.\n\n### Citation surfaces & controls in AI Search (2026)\n\nGoogle added many AI citation\u002Fsource surfaces across AI Overviews **and** AI Mode (May 2026):\n\n- **Preferred Sources**, users pick sites that get a \"preferred\" badge in AI answers; all-languages since 2026-04-30 (>345K sources selected); Google is working toward using it as a ranking signal. *Quick win:* encourage your audience to add the brand as a Preferred Source.\n- **\"Highly Cited\" badges**, earned via original primary reporting that other articles cite.\n- **Community Perspectives**, elevates Reddit\u002Fforum\u002Ffirsthand content.\n- Inline links, desktop hover **Link Previews**, and prominent link carousels.\n\n**Controlling AI-feature appearance:** there is **no AI-specific opt-out file**. Appearance in AI Overviews and AI Mode is governed by standard preview\u002Findex directives, `nosnippet`, `data-nosnippet`, `max-snippet`, `noindex` (distinct from the third-party AI-crawler robots controls above). Source: developers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\n\n**Search agents (live, not just WebMCP):** Google's \"Information Agents\" run in the background to monitor topics, plus agentic booking\u002Fcalling for select categories (rolling out to US users, summer 2026), so agent-friendly-page optimization (real interactive elements, accessibility tree, layout stability) now matters for actions, not only citations.\n\n---\n\n## Output\n\nGenerate `GEO-ANALYSIS.md` with:\n\n1. **GEO Readiness Score: XX\u002F100**\n2. **Platform breakdown** (Google AIO, ChatGPT, Perplexity scores)\n3. **AI Crawler Access Status** (which crawlers allowed\u002Fblocked)\n4. **llms.txt Status** (present, missing, recommendations)\n5. **Brand Mention Analysis** (presence on Wikipedia, Reddit, YouTube, LinkedIn)\n6. **Passage-Level Citability** (optimal 134-167 word blocks identified)\n7. **Server-Side Rendering Check** (JavaScript dependency analysis)\n8. **Top 5 Highest-Impact Changes**\n9. **Schema Recommendations** (for AI discoverability)\n10. **Content Reformatting Suggestions** (specific passages to rewrite)\n\n---\n\n## Quick Wins\n\n1. Add \"What is [topic]?\" definition in first 60 words\n2. Create 134-167 word self-contained answer blocks\n3. Add question-based H2\u002FH3 headings\n4. Include specific statistics with sources\n5. Add publication\u002Fupdate dates\n6. Implement Person schema for authors\n7. Allow key AI crawlers in robots.txt\n\n## Medium Effort\n\n1. Create `\u002Fllms.txt` file (optional: ignored by Google Search; may help other AI crawlers)\n2. Add author bio with credentials + Wikipedia\u002FLinkedIn links\n3. Ensure server-side rendering for key content\n4. Build entity presence on Reddit, YouTube\n5. Add comparison tables with data\n6. Implement FAQ sections (structured, not schema for commercial sites)\n\n## High Impact\n\n1. Create original research\u002Fsurveys (unique citability)\n2. Build Wikipedia presence for brand\u002Fkey people\n3. Establish YouTube channel with content mentions\n4. Implement comprehensive entity linking (sameAs across platforms)\n5. Develop unique tools or calculators\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `ai_optimization_chat_gpt_scraper` to check what ChatGPT web search returns for target queries (real GEO visibility check) and `ai_opt_llm_ment_search` with `ai_opt_llm_ment_top_domains` for LLM mention tracking across AI platforms.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess site content. Suggest the user verify the URL and try again. |\n| AI crawlers blocked by robots.txt | Report exactly which crawlers are blocked and which are allowed. Provide specific robots.txt directives to add for enabling AI search visibility. |\n| No llms.txt found | Note the absence (optional file; Google Search ignores it) and provide a ready-to-use llms.txt template for non-Google AI crawlers. |\n| No structured data detected | Report the gap and provide specific schema recommendations (Article, Organization, Person) for improving AI discoverability. |\n\n## FLOW Framework Integration\n\nFor prompt-guided AI content optimization, use `\u002Fseo flow optimize \u003Curl>`, FLOW's 21 optimize-stage prompts complement GEO's citability and structure analysis with evidence-led AI prompts.\n","# AI Search \u002F GEO Optimization (May 2026)\n\n## Primary Source: Google's AI Optimization Guide\n\nGoogle's official position, published under Search Central docs:\n\n> \"Optimizing for generative AI search is **still SEO** from Google's\n> perspective. AEO and GEO are rebranded labels for the same work.\"\n\nRead `references\u002Fgoogle-ai-optimization-guide.md` for the full synthesis,\nmyth-busting list (`llms.txt`, chunking, AI-rephrasing, mention-farming,\nall rejected by Google as ineffective), and the Who\u002FHow\u002FWhy test for\ncontent quality.\n\nAudits should frame GEO findings as **SEO fundamentals applied to AI-search\nsurfaces**, not as a separate optimization discipline. When community\nrecommendations contradict Google's primary source, defer to Google and note\nthe contradiction in the report.\n\n## Key Statistics\n\n| Metric | Value | Source |\n|--------|-------|--------|\n| AI Overviews reach | 2.5 billion+ monthly active users, reported from Google I\u002FO 2026 keynote coverage; not confirmed on a Google-owned source; 200+ countries | Third-party I\u002FO reporting |\n| AI Overviews query coverage | ~50% of queries (third-party measurement; varies by country) | Industry data |\n| AI Mode monthly users | 1B+, reported from Google I\u002FO 2026 keynote coverage; not confirmed on a Google-owned source | Third-party I\u002FO reporting |\n| AI Mode model | custom version of Gemini 2.5 | Google |\n| AI-referred sessions growth | 527% (Jan-May 2025) | SparkToro |\n| ChatGPT weekly active users | 900 million | OpenAI |\n| Perplexity monthly queries | 500+ million | Perplexity |\n\n## Critical Insight: Brand Mentions > Backlinks\n\n**Brand mentions correlate 3x more strongly with AI visibility than backlinks.**\n(Ahrefs December 2025 study of 75,000 brands)\n\n| Signal | Correlation with AI Citations |\n|--------|------------------------------|\n| YouTube mentions | ~0.737 (strongest) |\n| Reddit mentions | High |\n| Wikipedia presence | High |\n| LinkedIn presence | Moderate |\n| Domain Rating (backlinks) | ~0.266 (weak) |\n\n**Only 11% of domains** are cited by both ChatGPT and Google AI Overviews for the same query, so platform-specific optimization is essential.\n\n---\n\n## GEO Analysis Criteria (Updated)\n\n### 1. Citability Score (25%)\n\n**Optimal passage length: 134-167 words** for AI citation. And **~44% of AI\ncitations come from the first 30% of a page** (SE Ranking study), front-load\nyour most citable, self-contained answer rather than burying it below the fold.\n\n**Strong signals:**\n- Clear, quotable sentences with specific facts\u002Fstatistics\n- Self-contained answer blocks (can be extracted without context)\n- Direct answer in first 40-60 words of section\n- Claims attributed with specific sources\n- Definitions following \"X is...\" or \"X refers to...\" patterns\n- Unique data points not found elsewhere\n\n**Weak signals:**\n- Vague, general statements\n- Opinion without evidence\n- Buried conclusions\n- No specific data points\n\n### 2. Structural Readability (20%)\n\n**92% of AI Overview citations come from top-10 ranking pages**, but 47% come from pages ranking below position 5, demonstrating different selection logic.\n\n**Strong signals:**\n- Clean H1->H2->H3 heading hierarchy\n- Question-based headings (matches query patterns)\n- Short paragraphs (2-4 sentences)\n- Tables for comparative data\n- Ordered\u002Funordered lists for step-by-step or multi-item content\n- FAQ sections with clear Q&A format\n\n**Weak signals:**\n- Wall of text with no structure\n- Inconsistent heading hierarchy\n- No lists or tables\n- Information buried in paragraphs\n\n### 3. Multi-Modal Content (15%)\n\nContent with multi-modal elements sees **156% higher selection rates**.\n\n**Check for:**\n- Text + relevant images\n- Video content (embedded or linked)\n- Infographics and charts\n- Interactive elements (calculators, tools)\n- Structured data supporting media\n\n### 4. Authority & Brand Signals (20%)\n\n**Strong signals:**\n- Author byline with credentials\n- Publication date and last-updated date\n- **Recency**, content under 3 months old is ~3x more likely to be cited in AI answers; pages left stale 6+ months lose citation eligibility (SE Ranking, 1.3M-citation study). A scheduled refresh program is one of the highest-leverage GEO plays.\n- Citations to primary sources (studies, official docs, data)\n- Organization credentials and affiliations\n- Expert quotes with attribution\n- Entity presence in Wikipedia, Wikidata\n- Mentions on Reddit, YouTube, LinkedIn\n\n**Weak signals:**\n- Anonymous authorship\n- No dates\n- No sources cited\n- No brand presence across platforms\n\n### 5. Technical Accessibility (20%)\n\n**AI crawlers do NOT execute JavaScript.** Server-side rendering is critical.\n\n**Check for:**\n- Server-side rendering (SSR) vs client-only content\n- AI crawler access in robots.txt\n- llms.txt file presence and configuration\n- RSL 1.0 licensing terms\n\n---\n\n## AI Crawler Detection\n\nCheck `robots.txt` for these AI crawlers:\n\n| Crawler | Owner | Purpose | Obeys robots.txt? |\n|---------|-------|---------|---|\n| GPTBot | OpenAI | ChatGPT web search | yes |\n| OAI-SearchBot | OpenAI | OpenAI search features | yes |\n| ChatGPT-User | OpenAI | ChatGPT browsing (user-triggered) | no (user-triggered) |\n| ClaudeBot | Anthropic | Claude web features | yes |\n| PerplexityBot | Perplexity | Perplexity AI search | yes |\n| CCBot | Common Crawl | Training data (often blocked) | yes |\n| anthropic-ai | Anthropic | Claude training | yes |\n| Bytespider | ByteDance | TikTok\u002FDouyin AI | yes |\n| cohere-ai | Cohere | Cohere models | yes |\n| Google-Extended | Google | Gemini\u002FVertex training & grounding opt-out | yes |\n| Google-CloudVertexBot | Google | Site-owner-requested Vertex AI Agent crawls | yes |\n| Google-Agent | Google | Agentic browsing (Project Mariner), acts for a user | **no (user-triggered)** |\n| Google-NotebookLM | Google | Fetches individual user-added source URLs | **no (user-triggered)** |\n| Google Messages | Google | User-triggered fetch | **no (user-triggered)** |\n\n**Recommendation:** Allow GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot for AI search visibility. Block CCBot and training crawlers if desired.\n\n> **User-triggered fetchers ignore robots.txt by design** (Google-Agent, Google-NotebookLM, Google Messages, ChatGPT-User). robots.txt cannot block them, use server-side access controls. Google's canonical crawling\u002Frobots reference moved to **developers.google.com\u002Fcrawling** (migrated 2025-11-20); IP-range files now live at `\u002Fcrawling\u002Fipranges\u002F` and `googlebot.json` was renamed `common-crawlers.json`. Emerging: **Web Bot Auth** (RFC 9421) lets bots authenticate via a `Signature-Agent` header + key directory (used by Google-Agent); reverse-DNS verification remains the fallback.\n\n---\n\n## llms.txt Standard\n\nRead `references\u002Fllmstxt-evidence.md` for the primary-source evidence (Mueller, Illyes, SE Ranking 300k-domain study, OtterlyAI server-log audit) on why `\u002Fllms.txt` is not currently a citation lever for major AI search systems. claude-seo reports presence but assigns no citation-ranking weight.\n\n> **Google now states this explicitly.** Google's AI optimization guide (updated 2026-06-29) says you do **not** need `llms.txt` \u002F AI-text files for Google Search, including its generative AI features, and that doing so \"won't harm (nor help) your visibility or rankings in Google Search, as Google Search ignores them.\" Mueller separately called the llms.txt discovery use case \"a dead end.\" It's fine to keep for **non-Google** AI services; never recommend it as a Google ranking\u002Fcitation lever. Source: developers.google.com\u002Fsearch\u002Fdocs\u002Ffundamentals\u002Fai-optimization-guide\n\nThe emerging **llms.txt** standard provides AI crawlers with structured content guidance.\n\n**Location:** `\u002Fllms.txt` (root of domain)\n\n**Format:**\n```\n# Title of site\n> Brief description\n\n## Main sections\n- [Page title](url): Description\n- [Another page](url): Description\n\n## Optional: Key facts\n- Fact 1\n- Fact 2\n```\n\n**Check for:**\n- Presence of `\u002Fllms.txt`\n- Structured content guidance\n- Key page highlights\n- Contact\u002Fauthority information\n\n---\n\n## RSL 1.0 (Really Simple Licensing)\n\nNew standard (December 2025) for machine-readable AI licensing terms.\n\n**Backed by:** Reddit, Yahoo, Medium, Quora, Cloudflare, Akamai, Creative Commons\n\n**Check for:** RSL implementation and appropriate licensing terms.\n\n---\n\n## Platform-Specific Optimization\n\n| Platform | Key Citation Sources | Optimization Focus |\n|----------|---------------------|-------------------|\n| **Google AI Overviews** | Strongly ranking-correlated, cites pages that already rank well | Traditional SEO + passage optimization |\n| **Google AI Mode** (custom version of Gemini 2.5) | Weakly ranking-correlated; broader pool (~9 domains cited\u002Fquery, Ahrefs) | Distinct surface: freshness, entity authority, citable passages beyond position 5 |\n| **ChatGPT** | Wikipedia (47.9%), Reddit (11.3%) | Entity presence, authoritative sources |\n| **Perplexity** | Reddit (46.7%), Wikipedia | Community validation, discussions |\n| **Bing Copilot** | Bing index, authoritative sites | Bing SEO, IndexNow |\n\n> **Two Google citation engines, not one.** AI Mode and AI Overviews reach the\n> same conclusion ~86% of the time but cite the same URLs only **13.7%** of the\n> time (Ahrefs study, 540K query pairs). Treat them as separate surfaces: ranking\n> well in classic Search feeds AI Overviews, but AI Mode draws from a broader pool\n> where freshness and entity authority outweigh raw position. Score both.\n>\n> **UX is now unified, surfaces still distinct.** At Google I\u002FO 2026 (2026-05-19)\n> Google merged AI Overviews and AI Mode into \"one seamless AI Search experience\"\n> (question → AI Overview → follow-up in AI Mode) with a new intelligent Search\n> box. The *experience* is one flow, but the two citation engines remain\n> technically distinct (different models\u002Flink sets), keep scoring both.\n\n### Citation surfaces & controls in AI Search (2026)\n\nGoogle added many AI citation\u002Fsource surfaces across AI Overviews **and** AI Mode (May 2026):\n\n- **Preferred Sources**, users pick sites that get a \"preferred\" badge in AI answers; all-languages since 2026-04-30 (>345K sources selected); Google is working toward using it as a ranking signal. *Quick win:* encourage your audience to add the brand as a Preferred Source.\n- **\"Highly Cited\" badges**, earned via original primary reporting that other articles cite.\n- **Community Perspectives**, elevates Reddit\u002Fforum\u002Ffirsthand content.\n- Inline links, desktop hover **Link Previews**, and prominent link carousels.\n\n**Controlling AI-feature appearance:** there is **no AI-specific opt-out file**. Appearance in AI Overviews and AI Mode is governed by standard preview\u002Findex directives, `nosnippet`, `data-nosnippet`, `max-snippet`, `noindex` (distinct from the third-party AI-crawler robots controls above). Source: developers.google.com\u002Fsearch\u002Fdocs\u002Fappearance\u002Fai-features\n\n**Search agents (live, not just WebMCP):** Google's \"Information Agents\" run in the background to monitor topics, plus agentic booking\u002Fcalling for select categories (rolling out to US users, summer 2026), so agent-friendly-page optimization (real interactive elements, accessibility tree, layout stability) now matters for actions, not only citations.\n\n---\n\n## Output\n\nGenerate `GEO-ANALYSIS.md` with:\n\n1. **GEO Readiness Score: XX\u002F100**\n2. **Platform breakdown** (Google AIO, ChatGPT, Perplexity scores)\n3. **AI Crawler Access Status** (which crawlers allowed\u002Fblocked)\n4. **llms.txt Status** (present, missing, recommendations)\n5. **Brand Mention Analysis** (presence on Wikipedia, Reddit, YouTube, LinkedIn)\n6. **Passage-Level Citability** (optimal 134-167 word blocks identified)\n7. **Server-Side Rendering Check** (JavaScript dependency analysis)\n8. **Top 5 Highest-Impact Changes**\n9. **Schema Recommendations** (for AI discoverability)\n10. **Content Reformatting Suggestions** (specific passages to rewrite)\n\n---\n\n## Quick Wins\n\n1. Add \"What is [topic]?\" definition in first 60 words\n2. Create 134-167 word self-contained answer blocks\n3. Add question-based H2\u002FH3 headings\n4. Include specific statistics with sources\n5. Add publication\u002Fupdate dates\n6. Implement Person schema for authors\n7. Allow key AI crawlers in robots.txt\n\n## Medium Effort\n\n1. Create `\u002Fllms.txt` file (optional: ignored by Google Search; may help other AI crawlers)\n2. Add author bio with credentials + Wikipedia\u002FLinkedIn links\n3. Ensure server-side rendering for key content\n4. Build entity presence on Reddit, YouTube\n5. Add comparison tables with data\n6. Implement FAQ sections (structured, not schema for commercial sites)\n\n## High Impact\n\n1. Create original research\u002Fsurveys (unique citability)\n2. Build Wikipedia presence for brand\u002Fkey people\n3. Establish YouTube channel with content mentions\n4. Implement comprehensive entity linking (sameAs across platforms)\n5. Develop unique tools or calculators\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `ai_optimization_chat_gpt_scraper` to check what ChatGPT web search returns for target queries (real GEO visibility check) and `ai_opt_llm_ment_search` with `ai_opt_llm_ment_top_domains` for LLM mention tracking across AI platforms.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess site content. Suggest the user verify the URL and try again. |\n| AI crawlers blocked by robots.txt | Report exactly which crawlers are blocked and which are allowed. Provide specific robots.txt directives to add for enabling AI search visibility. |\n| No llms.txt found | Note the absence (optional file; Google Search ignores it) and provide a ready-to-use llms.txt template for non-Google AI crawlers. |\n| No structured data detected | Report the gap and provide specific schema recommendations (Article, Organization, Person) for improving AI discoverability. |\n\n## FLOW Framework Integration\n\nFor prompt-guided AI content optimization, use `\u002Fseo flow optimize \u003Curl>`, FLOW's 21 optimize-stage prompts complement GEO's citability and structure analysis with evidence-led AI prompts.\n",{"name":5,"license":15,"metadata":65,"description":68,"argument-hint":69,"user-invocable":70},{"author":66,"version":67,"category":34},"AgriciDaniel","2.2.4","Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says \"AI Overviews\", \"SGE\", \"GEO\", \"AI search\", \"LLM optimization\", \"Perplexity\", \"AI citations\", \"ChatGPT search\", or \"AI visibility\".\n","[url]",true,"main","d8378e461d60315bd1ca8a6e71716df39b6abe4a","configs\u002Fcommon\u002Fclaude\u002Fskills\u002Fseo-geo","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup","https:\u002F\u002Fgithub.com\u002Fnoirelab","Artificial Intelligence Researcher |\r\nComputer Science majoring at UTFPR","2026-11-11 09:12:51",{"source":8,"skillName":5,"commands":79,"prompt":89,"zipAvailable":70},[80,83,86],{"tool":81,"command":82},"npx","npx skills add noirelab\u002Flinux-setup --skill seo-geo",{"tool":84,"command":85},"bunx","bunx skills add noirelab\u002Flinux-setup --skill seo-geo",{"tool":87,"command":88},"pnpm","pnpm dlx skills add noirelab\u002Flinux-setup --skill seo-geo","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fseo-geo\u002FSKILL.md",[91,96,102],{"path":92,"resourceType":93,"resourceTypeDesc":94,"sizeBytes":95,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",143,{"path":97,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fgoogle-ai-optimization-guide.md","REFERENCE","参考文档",6619,"https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fseo-geo\u002Freferences\u002Fgoogle-ai-optimization-guide.md",{"path":103,"resourceType":98,"resourceTypeDesc":99,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fllmstxt-evidence.md",3010,"https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fseo-geo\u002Freferences\u002Fllmstxt-evidence.md",[107,135,170,196,215,231],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":112,"sourceHtmlUrl":113,"categories":114,"keywords":118,"publishedAt":60},"01M6C2Q1S6HCS7Y9TBQ2PJ2NAM","skill-creator","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fsynced\u002Ffcfa9261-0073-49cb-8312-19eea68f0c58_391f3767-a35d-4280-81a1-363cd861a8e6\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fsynced\u002Ffcfa9261-0073-49cb-8312-19eea68f0c58_391f3767-a35d-4280-81a1-363cd861a8e6\u002Fskill-creator\u002FSKILL.md",[115],{"categoryNo":116,"categoryName":117,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",[119,120,121,122,123,124,125,126,127,50,128,129,45,130,44,131,39,132,133,37,134],"skill creator","creator","performance","run evals","test","variance analysis","create","run","benchmark","improve","edit","evals","existing skill","accuracy","scratch","new",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":60},"01M6C2ZRGJ9HN4N9BP24DPNCR4","returns-reverse-logistics","Codified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Freturns-reverse-logistics\u002FSKILL.md",[],[142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,45,158,159,160,161,162,163,164,165,166,39,167,168,37,169],"returns","returns reverse logistics","reverse logistics","reverse","github","code","product","https","authorization","detection","fraud","recovery","decisions","management","operations","vendor","claims management","frameworks","fraud detection","pattern","grading","processing","economics","managers","claims","expertise","refund","inspection",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":60},"01M6C2ZGWSNZGZZQ5VCFWHD2QT","quality-nonconformance","Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Fquality-nonconformance\u002FSKILL.md",[],[177,178,146,179,180,147,181,149,182,183,184,185,50,186,155,45,187,188,189,190,191,192,193,194,39,167,195,37],"quality","quality nonconformance","root cause analysis","interpretation","issues","audit","data","methodology","lifecycle","investigation","fda","across","manufacturing","environments","corrective action","supplier quality management","action","conformance","root cause",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":202,"publishedAt":60},"01M6C2Z8VQ9AYZYKPZW1KD9DX5","production-scheduling","Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC\u002Fdrum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP\u002FMES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Fproduction-scheduling\u002FSKILL.md",[],[203,204,205,146,147,206,149,53,50,207,208,45,209,210,159,211,212,213,189,214,39,167,37],"Production","production scheduling","scheduling","batch","erp","bottleneck","toc","patterns","line","interaction","sequencing","buffer",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":221,"publishedAt":60},"01M6C2YR01NNN2Z1G18RN7F9MP","logistics-exception-management","Codified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience. Includes escalation protocols, carrier-specific behaviors, claims procedures, and judgment frameworks. Use when handling shipping exceptions, freight claims, delivery issues, or carrier disputes.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Flogistics-exception-management\u002FSKILL.md",[],[155,222,146,147,223,181,224,149,225,45,159,226,227,228,229,166,39,167,230,37],"logistics exception management","shipping","protocols","escalation","delivery","claude-code","judgment","with","freight",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":235,"categories":236,"keywords":237,"publishedAt":60},"01M6C2YC5PXTEJYBJJQZVXPQS0","inventory-demand-planning","Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC\u002FXYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Finventory-demand-planning\u002FSKILL.md",[],[238,239,240,241,146,147,149,242,53,243,50,155,157,244,45,245,159,246,247,248,249,250,39,167,251,37],"planning","inventory","inventory demand planning","demand planning","negotiation","Safety","forecasting","selection","estimation","stock","seasonal","lift","at","transition"]