[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DZ05883RPSWRV360MWRMTC":3,"skill-same:01M6DZ05883RPSWRV360MWRMTC":66},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M6DZ05883RPSWRV360MWRMTC","rag-engineer",null,"Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.","Aceryoung\u002Ffamilyproject","Aceryoung","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F75876477?v=4","01M6DY8Q14MHFC0JX3G14KK9CW","kkong","01M6DY8Q58SY27PE2AH6GFZP9Y",0,"MIT","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Frag-engineer\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"rag engineer","engineer","rag","use","systems","generation","chunking","strategies","document","expert","models","databases","applications","Vector Search","Optimization","embeddings","vector","search","apache","semantic search","2026-11-10 10:12:30",false,"---\nname: rag-engineer\ndescription: \"Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.\"\nsource: vibeship-spawner-skills (Apache 2.0)\n---\n\n# RAG Engineer\n\n**Role**: RAG Systems Architect\n\nI bridge the gap between raw documents and LLM understanding. I know that\nretrieval quality determines generation quality - garbage in, garbage out.\nI obsess over chunking boundaries, embedding dimensions, and similarity\nmetrics because they make the difference between helpful and hallucinating.\n\n## Capabilities\n\n- Vector embeddings and similarity search\n- Document chunking and preprocessing\n- Retrieval pipeline design\n- Semantic search implementation\n- Context window optimization\n- Hybrid search (keyword + semantic)\n\n## Requirements\n\n- LLM fundamentals\n- Understanding of embeddings\n- Basic NLP concepts\n\n## Patterns\n\n### Semantic Chunking\n\nChunk by meaning, not arbitrary token counts\n\n```javascript\n- Use sentence boundaries, not token limits\n- Detect topic shifts with embedding similarity\n- Preserve document structure (headers, paragraphs)\n- Include overlap for context continuity\n- Add metadata for filtering\n```\n\n### Hierarchical Retrieval\n\nMulti-level retrieval for better precision\n\n```javascript\n- Index at multiple chunk sizes (paragraph, section, document)\n- First pass: coarse retrieval for candidates\n- Second pass: fine-grained retrieval for precision\n- Use parent-child relationships for context\n```\n\n### Hybrid Search\n\nCombine semantic and keyword search\n\n```javascript\n- BM25\u002FTF-IDF for keyword matching\n- Vector similarity for semantic matching\n- Reciprocal Rank Fusion for combining scores\n- Weight tuning based on query type\n```\n\n## Anti-Patterns\n\n### ❌ Fixed Chunk Size\n\n### ❌ Embedding Everything\n\n### ❌ Ignoring Evaluation\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Fixed-size chunking breaks sentences and context | high | Use semantic chunking that respects document structure: |\n| Pure semantic search without metadata pre-filtering | medium | Implement hybrid filtering: |\n| Using same embedding model for different content types | medium | Evaluate embeddings per content type: |\n| Using first-stage retrieval results directly | medium | Add reranking step: |\n| Cramming maximum context into LLM prompt | medium | Use relevance thresholds: |\n| Not measuring retrieval quality separately from generation | high | Separate retrieval evaluation: |\n| Not updating embeddings when source documents change | medium | Implement embedding refresh: |\n| Same retrieval strategy for all query types | medium | Implement hybrid search: |\n\n## Related Skills\n\nWorks well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend`\n","# RAG Engineer\n\n**Role**: RAG Systems Architect\n\nI bridge the gap between raw documents and LLM understanding. I know that\nretrieval quality determines generation quality - garbage in, garbage out.\nI obsess over chunking boundaries, embedding dimensions, and similarity\nmetrics because they make the difference between helpful and hallucinating.\n\n## Capabilities\n\n- Vector embeddings and similarity search\n- Document chunking and preprocessing\n- Retrieval pipeline design\n- Semantic search implementation\n- Context window optimization\n- Hybrid search (keyword + semantic)\n\n## Requirements\n\n- LLM fundamentals\n- Understanding of embeddings\n- Basic NLP concepts\n\n## Patterns\n\n### Semantic Chunking\n\nChunk by meaning, not arbitrary token counts\n\n```javascript\n- Use sentence boundaries, not token limits\n- Detect topic shifts with embedding similarity\n- Preserve document structure (headers, paragraphs)\n- Include overlap for context continuity\n- Add metadata for filtering\n```\n\n### Hierarchical Retrieval\n\nMulti-level retrieval for better precision\n\n```javascript\n- Index at multiple chunk sizes (paragraph, section, document)\n- First pass: coarse retrieval for candidates\n- Second pass: fine-grained retrieval for precision\n- Use parent-child relationships for context\n```\n\n### Hybrid Search\n\nCombine semantic and keyword search\n\n```javascript\n- BM25\u002FTF-IDF for keyword matching\n- Vector similarity for semantic matching\n- Reciprocal Rank Fusion for combining scores\n- Weight tuning based on query type\n```\n\n## Anti-Patterns\n\n### ❌ Fixed Chunk Size\n\n### ❌ Embedding Everything\n\n### ❌ Ignoring Evaluation\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Fixed-size chunking breaks sentences and context | high | Use semantic chunking that respects document structure: |\n| Pure semantic search without metadata pre-filtering | medium | Implement hybrid filtering: |\n| Using same embedding model for different content types | medium | Evaluate embeddings per content type: |\n| Using first-stage retrieval results directly | medium | Add reranking step: |\n| Cramming maximum context into LLM prompt | medium | Use relevance thresholds: |\n| Not measuring retrieval quality separately from generation | high | Separate retrieval evaluation: |\n| Not updating embeddings when source documents change | medium | Implement embedding refresh: |\n| Same retrieval strategy for all query types | medium | Implement hybrid search: |\n\n## Related Skills\n\nWorks well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend`\n",{"name":5,"source":44,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","483e87f6ad1460beac541f70200e988102e5c385","antigravity-awesome-skills-main\u002Fskills\u002Frag-engineer","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject","https:\u002F\u002Ffamilyproject-iota.vercel.app","https:\u002F\u002Fgithub.com\u002FAceryoung","2026-09-04 09:36:44",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add Aceryoung\u002Ffamilyproject --skill rag-engineer",{"tool":58,"command":59},"bunx","bunx skills add Aceryoung\u002Ffamilyproject --skill rag-engineer",{"tool":61,"command":62},"pnpm","pnpm dlx skills add Aceryoung\u002Ffamilyproject --skill rag-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Frag-engineer\u002FSKILL.md",true,[],[67,97,129,149,163,188],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":78,"publishedAt":39},"01M6DZ9PNJVNQ1M7196KMPEHZC","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[73],{"categoryNo":74,"categoryName":75,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[79,80,81,82,83,84,85,86,87,88,89,90,22,91,92,93,37,94,95,96],"zapier make patterns","patterns","workflow","integrations","how to","apps","platform","but","build","complexity","power","simplicity","business","automate","how","code","automation","writing",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":109,"publishedAt":39},"01M6DZ9K3385QP8QC2F3ZHZH1W","Cross-Site Scripting and HTML Injection Testing","This skill should be used when the user asks to \"test for XSS vulnerabilities\", \"perform cross-site scripting attacks\", \"identify HTML injection flaws\", \"exploit client-side injection vulnerabilities\", \"steal cookies via XSS\", or \"bypass content security policies\". It provides comprehensive techniques for detecting, exploiting, and understanding XSS and HTML injection attack vectors in web applications.","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fxss-html-injection\u002FSKILL.md",[103,106],{"categoryNo":104,"categoryName":105,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":107,"categoryName":108,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000009","前端开发",[110,111,112,113,114,115,116,117,118,31,119,120,121,122,123,124,125,126,127,128],"html injection testing","Cross Site Scripting and HTML Injection Testing","injection","scripting","site","html","testing","vectors","exploit","xss","cookies","content","web","test","steal","client","via","security","vulnerabilities",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":144,"publishedAt":39},"01M6DZ9B72D51Q21DTFD63TVEN","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[135,136,139],{"categoryNo":104,"categoryName":105,"taxonomyType":76,"taxonomyTypeDesc":77},{"categoryNo":137,"categoryName":138,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[96,145,146,22,147,148],"writing skills","deployment","editing","work",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":157,"publishedAt":39},"01M6DZ98HBB74FXZS78BATF6P5","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fwriting-plans\u002FSKILL.md",[155,156],{"categoryNo":137,"categoryName":138,"taxonomyType":76,"taxonomyTypeDesc":77},{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},[158,96,159,160,161,22,94,162],"writing plans","plans","requirements","task","spec",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":170,"publishedAt":39},"01M6DZ94C3FCDXCTV795CTRP1V","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fworkflow-automation\u002FSKILL.md",[169],{"categoryNo":74,"categoryName":75,"taxonomyType":76,"taxonomyTypeDesc":77},[171,95,81,172,173,174,37,175,176,177,178,179,180,181,182,183,184,185,186,187],"Workflow Automation","Production","flow","network","workflows","accessibility","ai agents","execution","scripts","worker","payment","n8n","money","resume","orchestrator","parallel","inngest",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":195,"publishedAt":39},"01M6DZ90RQ8GCWE9CGPCMHMNDD","WordPress Penetration Testing","This skill should be used when the user asks to \"pentest WordPress sites\", \"scan WordPress for vulnerabilities\", \"enumerate WordPress users, themes, or plugins\", \"exploit WordPress vulnerabilities\", or \"use WPScan\". It provides comprehensive WordPress security assessment methodologies.","https:\u002F\u002Fgithub.com\u002FAceryoung\u002Ffamilyproject\u002Fblob\u002Fmain\u002Fantigravity-awesome-skills-main\u002Fskills\u002Fwordpress-penetration-testing\u002FSKILL.md",[194],{"categoryNo":104,"categoryName":105,"taxonomyType":76,"taxonomyTypeDesc":77},[116,190,196,197,198,199,200,201,118,22,202,203,204,128,127],"penetration testing","wordpress","security assessment","sites","scan","pentest","user","plugins","assessment"]