[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PRXMTBQFBFNEMNTQQRE1NB":3,"skill-same:01M6PRXMTBQFBFNEMNTQQRE1NB":88},{"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":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":14,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":6,"updatedAt":68,"install":69,"resources":81},"01M6PRXMTBQFBFNEMNTQQRE1NB","seo-content",null,"Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says \"content quality\", \"E-E-A-T\", \"content analysis\", \"readability check\", \"thin content\", or \"content audit\".","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fseo-content\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"content","seo","seo content","use","content analysis","true","e-e-a-t","URL","with","readiness","readability","Citation","content audit","user","check","quality","analysis","audit","assessment","2026-11-10 10:12:30",false,"---\nname: seo-content\ndescription: >\n  Content quality and E-E-A-T analysis with AI citation readiness assessment.\n  Use when user says \"content quality\", \"E-E-A-T\", \"content analysis\",\n  \"readability check\", \"thin content\", or \"content audit\".\nuser-invocable: true\nargument-hint: \"[url]\"\nlicense: MIT\nmetadata:\n  author: AgriciDaniel\n  version: \"2.2.0\"\n  category: seo\n---\n\n# Content Quality & E-E-A-T Analysis\n\n## Google's \"Who \u002F How \u002F Why\" Test (canonical heuristic)\n\nBefore scoring E-E-A-T sub-factors, every page audit should pass Google's\nown three-question heuristic from the helpful-content guide:\n\n| Question | What to look for |\n|---|---|\n| **Who** created it? | Visible byline, author bio page, professional credentials. Required where readers expect it; non-negotiable for YMYL. |\n| **How** was it created? | Process disclosure where readers would reasonably ask — especially for AI-assisted content. Original research \u002F first-hand evidence \u002F lived experience. |\n| **Why** does it exist? | \"To help people\" rather than \"to attract search clicks.\" Watch for niche entry without expertise, content churn for freshness signals, content written to a word-count target. |\n\nPrimary source:\nhttps:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Ffundamentals\u002Fcreating-helpful-content\n\nWhen all three answers are weak, the page is at risk under the core ranking\nsystem's helpfulness signals (formerly the standalone Helpful Content System,\nmerged into core during the March 2024 update).\n\n## E-E-A-T Framework (updated Sept 2025 QRG)\n\nRead `skills\u002Fseo\u002Freferences\u002Feeat-framework.md` for full criteria.\n\n### Experience (first-hand signals)\n- Original research, case studies, before\u002Fafter results\n- Personal anecdotes, process documentation\n- Unique data, proprietary insights\n- Photos\u002Fvideos from direct experience\n\n### Expertise\n- Author credentials, certifications, bio\n- Professional background relevant to topic\n- Technical depth appropriate for audience\n- Accurate, well-sourced claims\n\n### Authoritativeness\n- External citations, backlinks from authoritative sources\n- Brand mentions, industry recognition\n- Published in recognized outlets\n- Cited by other experts\n\n### Trustworthiness\n- Contact information, physical address\n- Privacy policy, terms of service\n- Customer testimonials, reviews\n- Date stamps, transparent corrections\n- Secure site (HTTPS)\n\n## Content Metrics\n\n### Word Count Analysis\nCompare against page type minimums:\n| Page Type | Minimum |\n|-----------|---------|\n| Homepage | 500 |\n| Service page | 800 |\n| Blog post | 1,500 |\n| Product page | 300+ (400+ for complex products) |\n| Location page | 500-600 |\n\n> **Important:** These are **topical coverage floors**, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.\n\n### Readability\n- Flesch Reading Ease: target 60-70 for general audience\n\n> **Note:** Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.\n- Grade level: match target audience\n- Sentence length: average 15-20 words\n- Paragraph length: 2-4 sentences\n\n### Keyword Optimization\n- Primary keyword in title, H1, first 100 words\n- Natural density (1-3%)\n- Semantic variations present\n- No keyword stuffing\n\n### Content Structure\n- Logical heading hierarchy (H1 -> H2 -> H3)\n- Scannable sections with descriptive headings\n- Bullet\u002Fnumbered lists where appropriate\n- Table of contents for long-form content\n\n### Multimedia\n- Relevant images with proper alt text\n- Videos where appropriate\n- Infographics for complex data\n- Charts\u002Fgraphs for statistics\n\n### Internal Linking\n- 3-5 relevant internal links per 1000 words\n- Descriptive anchor text\n- Links to related content\n- No orphan pages\n\n### External Linking\n- Cite authoritative sources\n- Open in new tab for user experience\n- Reasonable count (not excessive)\n\n## AI Content Assessment (Sept 2025 QRG addition)\n\nGoogle's raters now formally assess whether content appears AI-generated.\n\n### Acceptable AI Content\n- Demonstrates genuine E-E-A-T\n- Provides unique value\n- Has human oversight and editing\n- Contains original insights\n\n### Low-Quality AI Content Markers\n- Generic phrasing, lack of specificity\n- No original insight\n- Repetitive structure across pages\n- No author attribution\n- Factual inaccuracies\n\n> **Helpful Content System (March 2024):** The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates.\n\n## AI Citation Readiness (GEO signals)\n\nOptimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):\n\n- Clear, quotable statements with statistics\u002Ffacts\n- Structured data (especially for data points)\n- Strong heading hierarchy (H1->H2->H3 flow)\n- Answer-first formatting for key questions\n- Tables and lists for comparative data\n- Clear attribution and source citations\n\n### AI Search Visibility & GEO (2025-2026)\n\n**Google AI Mode** is Google's conversational AI search surface — powered by **Gemini 3.5 Flash** since I\u002FO 2026 (May 2026) and now past **1 billion monthly users** globally. Unlike AI Overviews (which appear above organic results), AI Mode is a fully conversational experience with **zero organic blue links**, making AI citation the only visibility mechanism. It is a *distinct citation engine* from AI Overviews — the two share only ~14% of cited URLs — so optimize for both surfaces, not one (see the `seo-geo` skill).\n\n**Key optimization strategies for AI citation:**\n- **Structured answers:** Clear question-answer formats, definition patterns, and step-by-step instructions that AI systems can extract and cite\n- **First-party data:** Original research, statistics, case studies, and unique datasets are highly cited by AI systems\n- **Schema markup:** Article, FAQPage (Google retired FAQ *rich results* in May 2026, but the markup still aids AI parsing\u002Fentity resolution) or QAPage for genuine user Q&A, and structured content schemas help AI systems parse and attribute content\n- **Topical authority:** AI systems preferentially cite sources that demonstrate deep expertise. Build content clusters, not isolated pages\n- **Entity clarity:** Ensure brand, authors, and key concepts are clearly defined with structured data (Organization, Person schema)\n- **Multi-platform tracking:** Monitor visibility across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Bing Copilot, not just traditional rankings. Treat AI citation as a standalone KPI alongside organic rankings and traffic.\n\n**Generative Engine Optimization (GEO):**\nPer Google's AI optimization guide, \"AEO\" and \"GEO\" are rebranded labels for SEO — AI Overviews and AI Mode are grounded in the same ranking and quality systems as classic Search. The optimization signals that matter (quotability, attribution, heading hierarchy, freshness) are SEO fundamentals applied to AI-search surfaces, not a separate discipline. Cross-reference the `seo-geo` skill for detailed workflows; both surfaces share the primary-source synthesis in `skills\u002Fseo-geo\u002Freferences\u002Fgoogle-ai-optimization-guide.md`.\n\n## Content Freshness\n\n- Publication date visible\n- Last updated date if content has been revised\n- Flag content older than 12 months without update for fast-changing topics\n\n## Output\n\n### Content Quality Score: XX\u002F100\n\n### E-E-A-T Breakdown\n| Factor | Score | Key Signals |\n|--------|-------|-------------|\n| Experience | XX\u002F25 | ... |\n| Expertise | XX\u002F25 | ... |\n| Authoritativeness | XX\u002F25 | ... |\n| Trustworthiness | XX\u002F25 | ... |\n\n### AI Citation Readiness: XX\u002F100\n\n### Issues Found\n### Recommendations\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `kw_data_google_ads_search_volume` for real keyword volume data, `dataforseo_labs_bulk_keyword_difficulty` for difficulty scores, `dataforseo_labs_search_intent` for intent classification, and `content_analysis_summary` for content quality analysis.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |\n| Content behind paywall (402\u002F403, login wall) | Report that the content is not publicly accessible. Analyze only the visible portion (meta tags, headers) and note the limitation. |\n| Thin content (fewer than 100 words retrievable) | Report the findings as-is rather than guessing. Flag the page as potentially JavaScript-rendered or gated, and suggest the user provide the full text directly. |\n\n## FLOW Framework Integration\n\nFor prompt-guided content optimization, use `\u002Fseo flow optimize \u003Curl>` and `\u002Fseo flow win \u003Curl>` — FLOW's optimize and win prompts provide structured E-E-A-T improvement and BOFU conversion workflows.\n","# Content Quality & E-E-A-T Analysis\n\n## Google's \"Who \u002F How \u002F Why\" Test (canonical heuristic)\n\nBefore scoring E-E-A-T sub-factors, every page audit should pass Google's\nown three-question heuristic from the helpful-content guide:\n\n| Question | What to look for |\n|---|---|\n| **Who** created it? | Visible byline, author bio page, professional credentials. Required where readers expect it; non-negotiable for YMYL. |\n| **How** was it created? | Process disclosure where readers would reasonably ask — especially for AI-assisted content. Original research \u002F first-hand evidence \u002F lived experience. |\n| **Why** does it exist? | \"To help people\" rather than \"to attract search clicks.\" Watch for niche entry without expertise, content churn for freshness signals, content written to a word-count target. |\n\nPrimary source:\nhttps:\u002F\u002Fdevelopers.google.com\u002Fsearch\u002Fdocs\u002Ffundamentals\u002Fcreating-helpful-content\n\nWhen all three answers are weak, the page is at risk under the core ranking\nsystem's helpfulness signals (formerly the standalone Helpful Content System,\nmerged into core during the March 2024 update).\n\n## E-E-A-T Framework (updated Sept 2025 QRG)\n\nRead `skills\u002Fseo\u002Freferences\u002Feeat-framework.md` for full criteria.\n\n### Experience (first-hand signals)\n- Original research, case studies, before\u002Fafter results\n- Personal anecdotes, process documentation\n- Unique data, proprietary insights\n- Photos\u002Fvideos from direct experience\n\n### Expertise\n- Author credentials, certifications, bio\n- Professional background relevant to topic\n- Technical depth appropriate for audience\n- Accurate, well-sourced claims\n\n### Authoritativeness\n- External citations, backlinks from authoritative sources\n- Brand mentions, industry recognition\n- Published in recognized outlets\n- Cited by other experts\n\n### Trustworthiness\n- Contact information, physical address\n- Privacy policy, terms of service\n- Customer testimonials, reviews\n- Date stamps, transparent corrections\n- Secure site (HTTPS)\n\n## Content Metrics\n\n### Word Count Analysis\nCompare against page type minimums:\n| Page Type | Minimum |\n|-----------|---------|\n| Homepage | 500 |\n| Service page | 800 |\n| Blog post | 1,500 |\n| Product page | 300+ (400+ for complex products) |\n| Location page | 500-600 |\n\n> **Important:** These are **topical coverage floors**, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.\n\n### Readability\n- Flesch Reading Ease: target 60-70 for general audience\n\n> **Note:** Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.\n- Grade level: match target audience\n- Sentence length: average 15-20 words\n- Paragraph length: 2-4 sentences\n\n### Keyword Optimization\n- Primary keyword in title, H1, first 100 words\n- Natural density (1-3%)\n- Semantic variations present\n- No keyword stuffing\n\n### Content Structure\n- Logical heading hierarchy (H1 -> H2 -> H3)\n- Scannable sections with descriptive headings\n- Bullet\u002Fnumbered lists where appropriate\n- Table of contents for long-form content\n\n### Multimedia\n- Relevant images with proper alt text\n- Videos where appropriate\n- Infographics for complex data\n- Charts\u002Fgraphs for statistics\n\n### Internal Linking\n- 3-5 relevant internal links per 1000 words\n- Descriptive anchor text\n- Links to related content\n- No orphan pages\n\n### External Linking\n- Cite authoritative sources\n- Open in new tab for user experience\n- Reasonable count (not excessive)\n\n## AI Content Assessment (Sept 2025 QRG addition)\n\nGoogle's raters now formally assess whether content appears AI-generated.\n\n### Acceptable AI Content\n- Demonstrates genuine E-E-A-T\n- Provides unique value\n- Has human oversight and editing\n- Contains original insights\n\n### Low-Quality AI Content Markers\n- Generic phrasing, lack of specificity\n- No original insight\n- Repetitive structure across pages\n- No author attribution\n- Factual inaccuracies\n\n> **Helpful Content System (March 2024):** The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates.\n\n## AI Citation Readiness (GEO signals)\n\nOptimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):\n\n- Clear, quotable statements with statistics\u002Ffacts\n- Structured data (especially for data points)\n- Strong heading hierarchy (H1->H2->H3 flow)\n- Answer-first formatting for key questions\n- Tables and lists for comparative data\n- Clear attribution and source citations\n\n### AI Search Visibility & GEO (2025-2026)\n\n**Google AI Mode** is Google's conversational AI search surface — powered by **Gemini 3.5 Flash** since I\u002FO 2026 (May 2026) and now past **1 billion monthly users** globally. Unlike AI Overviews (which appear above organic results), AI Mode is a fully conversational experience with **zero organic blue links**, making AI citation the only visibility mechanism. It is a *distinct citation engine* from AI Overviews — the two share only ~14% of cited URLs — so optimize for both surfaces, not one (see the `seo-geo` skill).\n\n**Key optimization strategies for AI citation:**\n- **Structured answers:** Clear question-answer formats, definition patterns, and step-by-step instructions that AI systems can extract and cite\n- **First-party data:** Original research, statistics, case studies, and unique datasets are highly cited by AI systems\n- **Schema markup:** Article, FAQPage (Google retired FAQ *rich results* in May 2026, but the markup still aids AI parsing\u002Fentity resolution) or QAPage for genuine user Q&A, and structured content schemas help AI systems parse and attribute content\n- **Topical authority:** AI systems preferentially cite sources that demonstrate deep expertise. Build content clusters, not isolated pages\n- **Entity clarity:** Ensure brand, authors, and key concepts are clearly defined with structured data (Organization, Person schema)\n- **Multi-platform tracking:** Monitor visibility across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Bing Copilot, not just traditional rankings. Treat AI citation as a standalone KPI alongside organic rankings and traffic.\n\n**Generative Engine Optimization (GEO):**\nPer Google's AI optimization guide, \"AEO\" and \"GEO\" are rebranded labels for SEO — AI Overviews and AI Mode are grounded in the same ranking and quality systems as classic Search. The optimization signals that matter (quotability, attribution, heading hierarchy, freshness) are SEO fundamentals applied to AI-search surfaces, not a separate discipline. Cross-reference the `seo-geo` skill for detailed workflows; both surfaces share the primary-source synthesis in `skills\u002Fseo-geo\u002Freferences\u002Fgoogle-ai-optimization-guide.md`.\n\n## Content Freshness\n\n- Publication date visible\n- Last updated date if content has been revised\n- Flag content older than 12 months without update for fast-changing topics\n\n## Output\n\n### Content Quality Score: XX\u002F100\n\n### E-E-A-T Breakdown\n| Factor | Score | Key Signals |\n|--------|-------|-------------|\n| Experience | XX\u002F25 | ... |\n| Expertise | XX\u002F25 | ... |\n| Authoritativeness | XX\u002F25 | ... |\n| Trustworthiness | XX\u002F25 | ... |\n\n### AI Citation Readiness: XX\u002F100\n\n### Issues Found\n### Recommendations\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `kw_data_google_ads_search_volume` for real keyword volume data, `dataforseo_labs_bulk_keyword_difficulty` for difficulty scores, `dataforseo_labs_search_intent` for intent classification, and `content_analysis_summary` for content quality analysis.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |\n| Content behind paywall (402\u002F403, login wall) | Report that the content is not publicly accessible. Analyze only the visible portion (meta tags, headers) and note the limitation. |\n| Thin content (fewer than 100 words retrievable) | Report the findings as-is rather than guessing. Flag the page as potentially JavaScript-rendered or gated, and suggest the user provide the full text directly. |\n\n## FLOW Framework Integration\n\nFor prompt-guided content optimization, use `\u002Fseo flow optimize \u003Curl>` and `\u002Fseo flow win \u003Curl>` — FLOW's optimize and win prompts provide structured E-E-A-T improvement and BOFU conversion workflows.\n",{"name":5,"license":15,"metadata":56,"description":59,"argument-hint":60,"user-invocable":61},{"author":57,"version":58,"category":33},"AgriciDaniel","2.2.0","Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says \"content quality\", \"E-E-A-T\", \"content analysis\", \"readability check\", \"thin content\", or \"content audit\".\n","[url]",true,"main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".claude\u002Fskills\u002Fseo-content","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 00:42:44",{"source":8,"skillName":5,"commands":70,"prompt":80,"zipAvailable":61},[71,74,77],{"tool":72,"command":73},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill seo-content",{"tool":75,"command":76},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill seo-content",{"tool":78,"command":79},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill seo-content","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fseo-content\u002FSKILL.md",[82],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"LICENSE.txt","LICENSE","许可证",143,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fseo-content\u002FLICENSE.txt",[89,111,136,162,179,212],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":101,"publishedAt":51},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[95,98],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":99,"categoryName":100,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[102,103,104,105,45,35,106,107,108,109,110],"xunit","xunit skill","testing","injection","tests","triggers","unit","unit-testing","C#",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":119,"publishedAt":51},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[117,118],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":99,"categoryName":100,"taxonomyType":21,"taxonomyTypeDesc":22},[120,121,104,122,123,124,125,126,35,127,128,129,130,131,106,107,132,133,134,135],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","for","objective",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":146,"publishedAt":51},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[142,145],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":99,"categoryName":100,"taxonomyType":21,"taxonomyTypeDesc":22},[147,148,149,150,151,152,153,154,35,155,156,157,158,159,160,134,161],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":170,"publishedAt":51},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[168,169],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":99,"categoryName":100,"taxonomyType":21,"taxonomyTypeDesc":22},[171,104,172,122,173,174,175,176,45,35,177,130,106,107,39,178],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","conf",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":192,"publishedAt":51},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[185,188,189],{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":190,"categoryName":191,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师",[193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,107,211],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":220,"publishedAt":51},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[218,219],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":190,"categoryName":191,"taxonomyType":26,"taxonomyTypeDesc":27},[193,195,221,202,205,35,222,223,131,210,224,107,132,225,134,226,227],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]