[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M719MQR1B5VVT5KYAAAXESW6":3,"skill-same:01M719MQR1B5VVT5KYAAAXESW6":74},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":14,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M719MQR1B5VVT5KYAAAXESW6","rag-implementation",null,"RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Frag-implementation\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"rag","implementation","rag implementation","personal","bundle","setup","safe","retrieval","2026","generation","vector database","strategies","implementation workflow","workflow","database","selection","vector","Optimization","embedding","chunking","2026-11-10 10:12:30",false,"---\nname: rag-implementation\ndescription: \"RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.\"\ncategory: granular-workflow-bundle\nrisk: safe\nsource: personal\ndate_added: \"2026-02-27\"\n---\n\n# RAG Implementation Workflow\n\n## Overview\n\nSpecialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.\n\n## When to Use This Workflow\n\nUse this workflow when:\n- Building RAG-powered applications\n- Implementing semantic search\n- Creating knowledge-grounded AI\n- Setting up document Q&A systems\n- Optimizing retrieval quality\n\n## Workflow Phases\n\n### Phase 1: Requirements Analysis\n\n#### Skills to Invoke\n- `ai-product` - AI product design\n- `rag-engineer` - RAG engineering\n\n#### Actions\n1. Define use case\n2. Identify data sources\n3. Set accuracy requirements\n4. Determine latency targets\n5. Plan evaluation metrics\n\n#### Copy-Paste Prompts\n```\nUse @ai-product to define RAG application requirements\n```\n\n### Phase 2: Embedding Selection\n\n#### Skills to Invoke\n- `embedding-strategies` - Embedding selection\n- `rag-engineer` - RAG patterns\n\n#### Actions\n1. Evaluate embedding models\n2. Test domain relevance\n3. Measure embedding quality\n4. Consider cost\u002Flatency\n5. Select model\n\n#### Copy-Paste Prompts\n```\nUse @embedding-strategies to select optimal embedding model\n```\n\n### Phase 3: Vector Database Setup\n\n#### Skills to Invoke\n- `vector-database-engineer` - Vector DB\n- `similarity-search-patterns` - Similarity search\n\n#### Actions\n1. Choose vector database\n2. Design schema\n3. Configure indexes\n4. Set up connection\n5. Test queries\n\n#### Copy-Paste Prompts\n```\nUse @vector-database-engineer to set up vector database\n```\n\n### Phase 4: Chunking Strategy\n\n#### Skills to Invoke\n- `rag-engineer` - Chunking strategies\n- `rag-implementation` - RAG implementation\n\n#### Actions\n1. Choose chunk size\n2. Implement chunking\n3. Add overlap handling\n4. Create metadata\n5. Test retrieval quality\n\n#### Copy-Paste Prompts\n```\nUse @rag-engineer to implement chunking strategy\n```\n\n### Phase 5: Retrieval Implementation\n\n#### Skills to Invoke\n- `similarity-search-patterns` - Similarity search\n- `hybrid-search-implementation` - Hybrid search\n\n#### Actions\n1. Implement vector search\n2. Add keyword search\n3. Configure hybrid search\n4. Set up reranking\n5. Optimize latency\n\n#### Copy-Paste Prompts\n```\nUse @similarity-search-patterns to implement retrieval\n```\n\n```\nUse @hybrid-search-implementation to add hybrid search\n```\n\n### Phase 6: LLM Integration\n\n#### Skills to Invoke\n- `llm-application-dev-ai-assistant` - LLM integration\n- `llm-application-dev-prompt-optimize` - Prompt optimization\n\n#### Actions\n1. Select LLM provider\n2. Design prompt template\n3. Implement context injection\n4. Add citation handling\n5. Test generation quality\n\n#### Copy-Paste Prompts\n```\nUse @llm-application-dev-ai-assistant to integrate LLM\n```\n\n### Phase 7: Caching\n\n#### Skills to Invoke\n- `prompt-caching` - Prompt caching\n- `rag-engineer` - RAG optimization\n\n#### Actions\n1. Implement response caching\n2. Set up embedding cache\n3. Configure TTL\n4. Add cache invalidation\n5. Monitor hit rates\n\n#### Copy-Paste Prompts\n```\nUse @prompt-caching to implement RAG caching\n```\n\n### Phase 8: Evaluation\n\n#### Skills to Invoke\n- `llm-evaluation` - LLM evaluation\n- `evaluation` - AI evaluation\n\n#### Actions\n1. Define evaluation metrics\n2. Create test dataset\n3. Measure retrieval accuracy\n4. Evaluate generation quality\n5. Iterate on improvements\n\n#### Copy-Paste Prompts\n```\nUse @llm-evaluation to evaluate RAG system\n```\n\n## RAG Architecture\n\n```\nUser Query -> Embedding -> Vector Search -> Retrieved Docs -> LLM -> Response\n                |              |              |              |\n            Model         Vector DB     Chunk Store    Prompt + Context\n```\n\n## Quality Gates\n\n- [ ] Embedding model selected\n- [ ] Vector DB configured\n- [ ] Chunking implemented\n- [ ] Retrieval working\n- [ ] LLM integrated\n- [ ] Evaluation passing\n\n## Related Workflow Bundles\n\n- `ai-ml` - AI\u002FML development\n- `ai-agent-development` - AI agents\n- `database` - Vector databases\n","# RAG Implementation Workflow\n\n## Overview\n\nSpecialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.\n\n## When to Use This Workflow\n\nUse this workflow when:\n- Building RAG-powered applications\n- Implementing semantic search\n- Creating knowledge-grounded AI\n- Setting up document Q&A systems\n- Optimizing retrieval quality\n\n## Workflow Phases\n\n### Phase 1: Requirements Analysis\n\n#### Skills to Invoke\n- `ai-product` - AI product design\n- `rag-engineer` - RAG engineering\n\n#### Actions\n1. Define use case\n2. Identify data sources\n3. Set accuracy requirements\n4. Determine latency targets\n5. Plan evaluation metrics\n\n#### Copy-Paste Prompts\n```\nUse @ai-product to define RAG application requirements\n```\n\n### Phase 2: Embedding Selection\n\n#### Skills to Invoke\n- `embedding-strategies` - Embedding selection\n- `rag-engineer` - RAG patterns\n\n#### Actions\n1. Evaluate embedding models\n2. Test domain relevance\n3. Measure embedding quality\n4. Consider cost\u002Flatency\n5. Select model\n\n#### Copy-Paste Prompts\n```\nUse @embedding-strategies to select optimal embedding model\n```\n\n### Phase 3: Vector Database Setup\n\n#### Skills to Invoke\n- `vector-database-engineer` - Vector DB\n- `similarity-search-patterns` - Similarity search\n\n#### Actions\n1. Choose vector database\n2. Design schema\n3. Configure indexes\n4. Set up connection\n5. Test queries\n\n#### Copy-Paste Prompts\n```\nUse @vector-database-engineer to set up vector database\n```\n\n### Phase 4: Chunking Strategy\n\n#### Skills to Invoke\n- `rag-engineer` - Chunking strategies\n- `rag-implementation` - RAG implementation\n\n#### Actions\n1. Choose chunk size\n2. Implement chunking\n3. Add overlap handling\n4. Create metadata\n5. Test retrieval quality\n\n#### Copy-Paste Prompts\n```\nUse @rag-engineer to implement chunking strategy\n```\n\n### Phase 5: Retrieval Implementation\n\n#### Skills to Invoke\n- `similarity-search-patterns` - Similarity search\n- `hybrid-search-implementation` - Hybrid search\n\n#### Actions\n1. Implement vector search\n2. Add keyword search\n3. Configure hybrid search\n4. Set up reranking\n5. Optimize latency\n\n#### Copy-Paste Prompts\n```\nUse @similarity-search-patterns to implement retrieval\n```\n\n```\nUse @hybrid-search-implementation to add hybrid search\n```\n\n### Phase 6: LLM Integration\n\n#### Skills to Invoke\n- `llm-application-dev-ai-assistant` - LLM integration\n- `llm-application-dev-prompt-optimize` - Prompt optimization\n\n#### Actions\n1. Select LLM provider\n2. Design prompt template\n3. Implement context injection\n4. Add citation handling\n5. Test generation quality\n\n#### Copy-Paste Prompts\n```\nUse @llm-application-dev-ai-assistant to integrate LLM\n```\n\n### Phase 7: Caching\n\n#### Skills to Invoke\n- `prompt-caching` - Prompt caching\n- `rag-engineer` - RAG optimization\n\n#### Actions\n1. Implement response caching\n2. Set up embedding cache\n3. Configure TTL\n4. Add cache invalidation\n5. Monitor hit rates\n\n#### Copy-Paste Prompts\n```\nUse @prompt-caching to implement RAG caching\n```\n\n### Phase 8: Evaluation\n\n#### Skills to Invoke\n- `llm-evaluation` - LLM evaluation\n- `evaluation` - AI evaluation\n\n#### Actions\n1. Define evaluation metrics\n2. Create test dataset\n3. Measure retrieval accuracy\n4. Evaluate generation quality\n5. Iterate on improvements\n\n#### Copy-Paste Prompts\n```\nUse @llm-evaluation to evaluate RAG system\n```\n\n## RAG Architecture\n\n```\nUser Query -> Embedding -> Vector Search -> Retrieved Docs -> LLM -> Response\n                |              |              |              |\n            Model         Vector DB     Chunk Store    Prompt + Context\n```\n\n## Quality Gates\n\n- [ ] Embedding model selected\n- [ ] Vector DB configured\n- [ ] Chunking implemented\n- [ ] Retrieval working\n- [ ] LLM integrated\n- [ ] Evaluation passing\n\n## Related Workflow Bundles\n\n- `ai-ml` - AI\u002FML development\n- `ai-agent-development` - AI agents\n- `database` - Vector databases\n",{"name":5,"risk":33,"source":30,"category":52,"date_added":53,"description":7},"granular-workflow-bundle","2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fglobal\u002Frag-implementation","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill rag-implementation",{"tool":66,"command":67},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill rag-implementation",{"tool":69,"command":70},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill rag-implementation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Frag-implementation\u002FSKILL.md",true,[],[75,99,125,135,148,165],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":86,"publishedAt":47},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":84,"taxonomyTypeDesc":85},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[87,88,89,90,91,92,93,94,95,96,97,98],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch","with",{"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},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend tap', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[106,107,108,109,110,111,89,112,113,114,115,116,117,118,119,120,121,122,123,124],"trend","trend tap","tap","api keys","Bash","time","trending","hacker","aggregator","do","when","brew","live","data","google","install","read","across","api",{"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":47},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[132,133,106,117,118,119,120,110,89,122,134,123,124,116,114,113,112,121,111,109],"trend radar","radar","pulse",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":47},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[142,143,144,145,146,147],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":157,"publishedAt":47},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[154],{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[150,95,158,159,160,98,117,161,162,163,108,164,121],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":171,"publishedAt":47},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[167,172,173,174,121,175,162,176,177,178,179,180],"status","play","go","latest","github","discover","control","cmd","volume"]