[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CVP5T8ZYMAZRJSGXDNFSHN":3,"skill-same:01M6CVP5T8ZYMAZRJSGXDNFSHN":87},{"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":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":69,"repositoryHtmlUrl":70,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":6,"ownerDescription":72,"updatedAt":73,"install":74,"resources":86},"01M6CVP5T8ZYMAZRJSGXDNFSHN","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\".","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\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],"seo content","content","seo","quality","Citation","use","webfetch","user","read","check","grep","github","Bash","analysis","audit","https","assessment","content audit","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\".\nrisk: unknown\nsource: \"https:\u002F\u002Fgithub.com\u002FAgriciDaniel\u002Fclaude-seo\"\ndate_added: \"2026-03-21\"\nuser-invokable: true\nargument-hint: \"[url]\"\nallowed-tools:\n  - Read\n  - Grep\n  - Glob\n  - Bash\n  - WebFetch\n---\n\n# Content Quality & E-E-A-T Analysis\n\n## When to Use\n\n- Use when auditing content quality, readability, thin content risk, or E-E-A-T signals.\n- Use when the user wants a content-focused SEO review rather than a full technical audit.\n- Use when checking whether content is structured and trustworthy enough for search and AI citation.\n\n## E-E-A-T Framework (updated Sept 2025 QRG)\n\nRead `seo\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** launched publicly in May 2025 as a separate tab in Google Search, available in 180+ countries. Unlike AI Overviews (which appear above organic results), AI Mode provides a fully conversational search experience with **zero organic blue links**, making AI citation the only visibility mechanism.\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, FAQ (for non-Google AI platforms), 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):**\nGEO is the emerging discipline of optimizing content specifically for AI-generated answers. Key GEO signals include: quotability (clear, concise extractable facts), attribution (source citations within your content), structure (well-organized heading hierarchy), and freshness (regularly updated data). Cross-reference the `seo-geo` skill for detailed GEO workflows.\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","# Content Quality & E-E-A-T Analysis\n\n## When to Use\n\n- Use when auditing content quality, readability, thin content risk, or E-E-A-T signals.\n- Use when the user wants a content-focused SEO review rather than a full technical audit.\n- Use when checking whether content is structured and trustworthy enough for search and AI citation.\n\n## E-E-A-T Framework (updated Sept 2025 QRG)\n\nRead `seo\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** launched publicly in May 2025 as a separate tab in Google Search, available in 180+ countries. Unlike AI Overviews (which appear above organic results), AI Mode provides a fully conversational search experience with **zero organic blue links**, making AI citation the only visibility mechanism.\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, FAQ (for non-Google AI platforms), 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):**\nGEO is the emerging discipline of optimizing content specifically for AI-generated answers. Key GEO signals include: quotability (clear, concise extractable facts), attribution (source citations within your content), structure (well-organized heading hierarchy), and freshness (regularly updated data). Cross-reference the `seo-geo` skill for detailed GEO workflows.\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",{"name":5,"risk":55,"source":56,"date_added":57,"description":58,"allowed-tools":59,"argument-hint":64,"user-invokable":65},"unknown","https:\u002F\u002Fgithub.com\u002FAgriciDaniel\u002Fclaude-seo","2026-03-21","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",[60,61,62,44,63],"Read","Grep","Glob","WebFetch","[url]",true,"main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fseo-content",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":75,"prompt":85,"zipAvailable":65},[76,79,82],{"tool":77,"command":78},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill seo-content",{"tool":80,"command":81},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill seo-content",{"tool":83,"command":84},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill seo-content","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fseo-content\u002FSKILL.md",[],[88,99,116,143,162,189],{"skillNo":89,"name":90,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":96,"publishedAt":50},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[93],{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[97,98],"Test Skill","test",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":50},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[106,107,108,109,110,111,112,113,114,115],"zustand store ts","zustand","store","with","types","middleware","patterns","community","create","typescript",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":125,"publishedAt":50},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoom-automation\u002FSKILL.md",[122],{"categoryNo":123,"categoryName":124,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[126,127,128,129,130,131,132,133,134,135,136,137,138,139,113,140,141,142],"automation","zoom automation","zoom","first","Tools","search","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":50},"01M6GDRFS1452PC71JQ38S24HR","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[149],{"categoryNo":123,"categoryName":124,"taxonomyType":21,"taxonomyTypeDesc":22},[151,126,152,153,154,155,156,113,131,157,139,158,136,135,130,159,114,160,132,161],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"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":171,"publishedAt":50},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzod-validation-expert\u002FSKILL.md",[168],{"categoryNo":169,"categoryName":170,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[172,173,174,175,176,115,177,178,179,180,181,182,109,183,184,185,186,113,187,188],"expert","zod","zod validation expert","validation","react","safe","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":196,"publishedAt":50},"01M6GDR658B08K4MY0RWD4A6EH","zeroize-audit","Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C\u002FC++\u002FRust code handling secrets, keys, passwords, or other sensitive data.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzeroize-audit\u002FSKILL.md",[195],{"categoryNo":123,"categoryName":124,"taxonomyType":21,"taxonomyTypeDesc":22},[197,46,198,199,200,201,202,203,113,204,40,44,205,45,206,207,208,209,210,132],"zeroize audit","write","passwords","get","project","c++","activate","assembly","secrets","data","flow","verification","code","rust"]