[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GDFD0WJG6P7FE01SA42JXT":3,"skill-same:01M6GDFD0WJG6P7FE01SA42JXT":73},{"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":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6GDFD0WJG6P7FE01SA42JXT","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","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\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","search implementation","embedding","recommendation","vector databases","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\ndate_added: \"2026-02-27\"\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,"date_added":50,"description":7},"unknown","2026-02-27","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fvector-database-engineer",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":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill vector-database-engineer",{"tool":65,"command":66},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill vector-database-engineer",{"tool":68,"command":69},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill vector-database-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fvector-database-engineer\u002FSKILL.md",true,[],[74,85,101,127,146,172],{"skillNo":75,"name":76,"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":77,"categories":78,"keywords":82,"publishedAt":44},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[79],{"categoryNo":80,"categoryName":81,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[83,84],"Test Skill","test",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":44},"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",[],[92,93,94,95,96,97,98,37,99,100],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":110,"publishedAt":44},"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",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[111,112,113,114,115,42,116,117,118,119,120,121,122,123,37,124,125,126],"automation","zoom automation","zoom","first","Tools","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":134,"publishedAt":44},"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",[133],{"categoryNo":108,"categoryName":109,"taxonomyType":21,"taxonomyTypeDesc":22},[135,111,136,137,138,139,140,37,42,141,123,142,120,119,115,143,99,144,116,145],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":155,"publishedAt":44},"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",[152],{"categoryNo":153,"categoryName":154,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[29,156,157,158,159,100,160,161,162,163,164,165,95,166,167,168,169,37,170,171],"zod","zod validation expert","validation","react","safe","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":179,"publishedAt":44},"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",[178],{"categoryNo":108,"categoryName":109,"taxonomyType":21,"taxonomyTypeDesc":22},[180,181,182,183,184,185,186,187,37,188,189,190,191,192,193,194,195,196,197,116],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","data","flow","verification","code","rust"]