[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NTZYH553WWNP413FTC5TC0":3,"skill-same:01M6NTZYH553WWNP413FTC5TC0":79},{"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":28,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":14,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6NTZYH553WWNP413FTC5TC0","harvest-single",null,"Single page smart extraction - articles, docs, blog posts to clean markdown","rubicanjr\u002FFinCognis","rubicanjr","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F234912519?v=4","01M6NT156NND8J474G34SB3923","Rubi","01M6NT15B70RWXE1N3YV0BZY8A",0,"MIT","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fharvest-single\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"harvest single","harvest","write","single","posts","article","clean","webfetch","extract","docs","read","page","blog","markdown","Bash","scrape","extraction","documentation","2026-11-10 10:12:30",false,"---\nname: harvest-single\ndescription: Single page smart extraction - articles, docs, blog posts to clean markdown\nallowed-tools: [Bash, Read, Write, WebFetch]\nkeywords: [harvest, scrape, extract, single, page, article, documentation, markdown]\n---\n\n# Harvest Single Page\n\nExtract and clean content from a single web page. Auto-detects content type (article, documentation, API reference, blog post) and produces clean, structured markdown.\n\n## Usage\n\n```\n\u002Fharvest \u003Curl>\n```\n\n## Examples\n\n```bash\n# Extract a blog post\n\u002Fharvest https:\u002F\u002Fblog.example.com\u002Fbest-practices-2024\n\n# Extract API documentation page\n\u002Fharvest https:\u002F\u002Fdocs.stripe.com\u002Fapi\u002Fcharges\n\n# Extract a GitHub README\n\u002Fharvest https:\u002F\u002Fgithub.com\u002Fowner\u002Frepo\n```\n\n## How It Works\n\n1. Fetch URL content via WebFetch or crawl4ai\n2. Detect content type (article, docs, API ref, blog, wiki)\n3. Extract main content, strip navigation\u002Fads\u002Ffooters\n4. Preserve code blocks, tables, images\n5. Add metadata header (source, date, word count)\n6. Save to `.claude\u002Fcache\u002Fagents\u002Fharvest\u002F`\n\n## Output Format\n\n```markdown\n# [Page Title]\n> Source: [URL]\n> Extracted: [timestamp]\n> Type: [article|docs|api|blog|wiki]\n> Words: [count]\n\n[Clean extracted content in markdown]\n\n## Links Found\n- [Link text](URL)\n```\n\n## Fallback Chain\n\n1. crawl4ai Docker (port 11235) - preferred\n2. WebFetch tool - built-in fallback\n3. curl + html2text - last resort\n\n## When to Use\n\n- Quick grab of a single page's content\n- Extracting a specific doc page for reference\n- Saving an article for later analysis\n- Getting clean markdown from messy HTML\n","# Harvest Single Page\n\nExtract and clean content from a single web page. Auto-detects content type (article, documentation, API reference, blog post) and produces clean, structured markdown.\n\n## Usage\n\n```\n\u002Fharvest \u003Curl>\n```\n\n## Examples\n\n```bash\n# Extract a blog post\n\u002Fharvest https:\u002F\u002Fblog.example.com\u002Fbest-practices-2024\n\n# Extract API documentation page\n\u002Fharvest https:\u002F\u002Fdocs.stripe.com\u002Fapi\u002Fcharges\n\n# Extract a GitHub README\n\u002Fharvest https:\u002F\u002Fgithub.com\u002Fowner\u002Frepo\n```\n\n## How It Works\n\n1. Fetch URL content via WebFetch or crawl4ai\n2. Detect content type (article, docs, API ref, blog, wiki)\n3. Extract main content, strip navigation\u002Fads\u002Ffooters\n4. Preserve code blocks, tables, images\n5. Add metadata header (source, date, word count)\n6. Save to `.claude\u002Fcache\u002Fagents\u002Fharvest\u002F`\n\n## Output Format\n\n```markdown\n# [Page Title]\n> Source: [URL]\n> Extracted: [timestamp]\n> Type: [article|docs|api|blog|wiki]\n> Words: [count]\n\n[Clean extracted content in markdown]\n\n## Links Found\n- [Link text](URL)\n```\n\n## Fallback Chain\n\n1. crawl4ai Docker (port 11235) - preferred\n2. WebFetch tool - built-in fallback\n3. curl + html2text - last resort\n\n## When to Use\n\n- Quick grab of a single page's content\n- Extracting a specific doc page for reference\n- Saving an article for later analysis\n- Getting clean markdown from messy HTML\n",{"name":5,"keywords":52,"description":7,"allowed-tools":53},[30,44,37,32,40,34,46,42],[43,54,55,56],"Read","Write","WebFetch","main","70e1c350500ee750987420bbae48837afa906b7e","skills\u002Fharvest-single","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis","https:\u002F\u002Fgithub.com\u002Frubicanjr","linktr.ee\u002Ffinanskodu","Industrial Engineer & CIO @FinansKodu. Architecting AI-driven financial agents and autonomous workflows. 🏗️ Building systems, not just prompts.","2026-06-09 13:50:15",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add rubicanjr\u002FFinCognis --skill harvest-single",{"tool":71,"command":72},"bunx","bunx skills add rubicanjr\u002FFinCognis --skill harvest-single",{"tool":74,"command":75},"pnpm","pnpm dlx skills add rubicanjr\u002FFinCognis --skill harvest-single","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fharvest-single\u002FSKILL.md",true,[],[80,99,108,128,158,177],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":89,"publishedAt":47},"01M6NWS9DAXJTADT4T5MB49X8W","workflow-router","Goal-based workflow orchestration - routes tasks to specialist agents based on user goals","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-router\u002FSKILL.md",[86],{"categoryNo":87,"categoryName":88,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[90,91,92,93,94,95,96,97,98],"workflow router","workflow","router","orchestration","goals","goal","user","workflow orchestration","tasks",{"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":47},"01M6NWS3RETSC0SHP16HP66E14","wiring","Wiring Verification","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fwiring\u002FSKILL.md",[],[101,106,107],"verification","false",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":117,"publishedAt":47},"01M6NWS0XW9CVQZBDA1EGW0TWC","websocket-patterns","Connection management, room patterns, reconnection strategies, message buffering, and binary protocol design.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fwebsocket-patterns\u002Fskill.md",[114],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",[118,119,120,121,122,123,124,125,126,127],"websocket","patterns","websocket patterns","design","protocol","management","message","strategies","binary","connection",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":138,"publishedAt":47},"01M6NWRDQDX9ZNETAWRRMQWV52","visual-verdict","Screenshot comparison QA for frontend development. Takes a screenshot of the current implementation, scores it across multiple visual dimensions, and returns a structured PASS\u002FREVISE\u002FFAIL verdict with concrete fixes. Use when implementing UI from a design reference or verifying visual correctness.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fvisual-verdict\u002FSKILL.md",[134,137],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":87,"categoryName":88,"taxonomyType":21,"taxonomyTypeDesc":22},[139,140,141,142,143,144,145,146,147,148,149,150,151,121,152,153,154,155,156,157],"visual","verdict","visual verdict","ui","across","revise","comparison","correctness","implementing","for","fixes","dimensions","frontend","development","qa","screenshot","returns","use","frontend development",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":47},"01M6NWR8HSNRS7MNDK2J02QYMN","verification-loop","Comprehensive verification system covering build, types, lint, tests, security, and diff review before a PR.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fverification-loop\u002FSKILL.md",[],[165,166,106,167,168,169,170,171,172,173,174,175,176],"verification loop","loop","lint","review","security","pr","system","diff","build","tests","diff review","types",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":47},"01M6NWR4HHSFR0VR5EXFRWA747","vector-db-patterns","Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fvector-db-patterns\u002Fskill.md",[],[184,185,186,187,119,188,189,190,191,125,192,193,194,195,196,148,197,198],"vector db patterns","vector db","db","vector","semantic search","search","embedding strategies","algorithms","chunking","semantic","retrieval","embedding","Hybrid Search","hybrid","rag"]