[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FTP2VFBXGADYEPB3ETYD6D":3,"skill-same:01M6FTP2VFBXGADYEPB3ETYD6D":72},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6FTP2VFBXGADYEPB3ETYD6D","vector-database-engineer",null,"Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar","reikiplanet\u002Fdaoskills","reikiplanet","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F96377599?v=4","01M6CVWCMD740YHQDK7E9QYBW4","Reiki Planet","01M6CVWCS4KXVA2CWFVJ43EAXK",0,"MIT","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fdata\u002Fvector-database-engineer\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"database","vector","vector database","engineer","vector database engineer","expert","pgvector","embedding","recommendation","systems","strategies","weaviate","embedding strategies","community","rag","pinecone","databases","applications","search","semantic search","2026-11-10 10:12:30",false,"---\nname: vector-database-engineer\ndescription: \"Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar\"\nrisk: unknown\nsource: community\n---\n\n# Vector Database Engineer\n\nExpert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.\n\n## Do not use this skill when\n\n- The task is unrelated to vector database engineer\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Capabilities\n\n- Vector database selection and architecture\n- Embedding model selection and optimization\n- Index configuration (HNSW, IVF, PQ)\n- Hybrid search (vector + keyword) implementation\n- Chunking strategies for documents\n- Metadata filtering and pre\u002Fpost-filtering\n- Performance tuning and scaling\n\n## Use this skill when\n\n- Building RAG (Retrieval Augmented Generation) systems\n- Implementing semantic search over documents\n- Creating recommendation engines\n- Building image\u002Faudio similarity search\n- Optimizing vector search latency and recall\n- Scaling vector operations to millions of vectors\n\n## Workflow\n\n1. Analyze data characteristics and query patterns\n2. Select appropriate embedding model\n3. Design chunking and preprocessing pipeline\n4. Choose vector database and index type\n5. Configure metadata schema for filtering\n6. Implement hybrid search if needed\n7. Optimize for latency\u002Frecall tradeoffs\n8. Set up monitoring and reindexing strategies\n\n## Best Practices\n\n- Choose embedding dimensions based on use case (384-1536)\n- Implement proper chunking with overlap\n- Use metadata filtering to reduce search space\n- Monitor embedding drift over time\n- Plan for index rebuilding\n- Cache frequent queries\n- Test recall vs latency tradeoffs\n","# Vector Database Engineer\n\nExpert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.\n\n## Do not use this skill when\n\n- The task is unrelated to vector database engineer\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Capabilities\n\n- Vector database selection and architecture\n- Embedding model selection and optimization\n- Index configuration (HNSW, IVF, PQ)\n- Hybrid search (vector + keyword) implementation\n- Chunking strategies for documents\n- Metadata filtering and pre\u002Fpost-filtering\n- Performance tuning and scaling\n\n## Use this skill when\n\n- Building RAG (Retrieval Augmented Generation) systems\n- Implementing semantic search over documents\n- Creating recommendation engines\n- Building image\u002Faudio similarity search\n- Optimizing vector search latency and recall\n- Scaling vector operations to millions of vectors\n\n## Workflow\n\n1. Analyze data characteristics and query patterns\n2. Select appropriate embedding model\n3. Design chunking and preprocessing pipeline\n4. Choose vector database and index type\n5. Configure metadata schema for filtering\n6. Implement hybrid search if needed\n7. Optimize for latency\u002Frecall tradeoffs\n8. Set up monitoring and reindexing strategies\n\n## Best Practices\n\n- Choose embedding dimensions based on use case (384-1536)\n- Implement proper chunking with overlap\n- Use metadata filtering to reduce search space\n- Monitor embedding drift over time\n- Plan for index rebuilding\n- Cache frequent queries\n- Test recall vs latency tradeoffs\n",{"name":5,"risk":49,"source":37,"description":7},"unknown","master","dff58085da947e81e8418c922055644bf0082b1b","data\u002Fvector-database-engineer","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills","https:\u002F\u002Fgithub.com\u002Freikiplanet","https:\u002F\u002Fwww.reiki-planet.com","Reiki Planet Game","2026-03-29 06:02:29",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add reikiplanet\u002Fdaoskills --skill vector-database-engineer",{"tool":64,"command":65},"bunx","bunx skills add reikiplanet\u002Fdaoskills --skill vector-database-engineer",{"tool":67,"command":68},"pnpm","pnpm dlx skills add reikiplanet\u002Fdaoskills --skill vector-database-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fdata\u002Fvector-database-engineer\u002FSKILL.md",true,[],[73,99,111,125,145,168],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":44},"01M6G614JTEQJHPT8AKK3YQ18S","web-games","Web browser game development principles. 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Contains 12 templates for various tech stacks.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Ftemplates\u002FSKILL.md",[],[113,118,119,120,121,122,37,123,89,124,41,83],"project scaffolding","From Scratch","tech","project","scaffolding","use","projects",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":44},"01M6G60T0GATMK3Q5H8GDC1Z8S","wcag-audit-patterns","Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. 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OWASP 2025, Supply Chain Security, attack surface mapping, risk prioritization.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fvulnerability-scanner\u002FSKILL.md",[],[152,153,154,155,156,157,158,159,37,89,83,160,84,161,162,163,164,165,166,167],"scanner","vulnerability scanner","vulnerability","prioritization","2025","mapping","chain","supply chain","Bash","analysis","risk","owasp","supply chain security","security","supply","advanced",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":174,"publishedAt":44},"01M6G60JZBB8DK0W3DSG1WNMZK","threat-modeling-expert","Expert in threat modeling methodologies, security architecture review, and risk assessment. Masters STRIDE, PASTA, attack trees, and security requirement extraction. Use for security architecture r...","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fthreat-modeling-expert\u002FSKILL.md",[],[175,176,177,29,178,179,180,181,165,123,182,183,184,37,185,186,187,162,188,189],"modeling","threat modeling expert","threat modeling","threat","risk assessment","architecture review","architecture","security architecture review","trees","security architecture","security requirement extraction","assessment","extraction","review","stride"]