[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CVCV899PJ42WCBA99XKV1K":3,"skill-same:01M6CVCV899PJ42WCBA99XKV1K":59},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":40,"repositoryHtmlUrl":41,"repositoryHomepageUrl":41,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":43,"updatedAt":44,"install":45,"resources":58},"01M6CVCV899PJ42WCBA99XKV1K","rag-engineer",null,"Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.","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\u002Frag-engineer\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28],"rag","rag engineer","engineer","apache","vector","Optimization","applications","databases","models","expert","2026-11-10 10:12:30",false,"---\nname: rag-engineer\ndescription: Expert in building Retrieval-Augmented Generation systems. Masters\n  embedding models, vector databases, chunking strategies, and retrieval\n  optimization for LLM applications.\nrisk: unknown\nsource: vibeship-spawner-skills (Apache 2.0)\ndate_added: 2026-02-27\n---\n\n# RAG Engineer\n\nExpert in building Retrieval-Augmented Generation systems. Masters embedding models,\nvector databases, chunking strategies, and retrieval optimization for LLM applications.\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### Expertise\n\n- Embedding model selection and fine-tuning\n- Vector database architecture and scaling\n- Chunking strategies for different content types\n- Retrieval quality optimization\n- Hybrid search implementation\n- Re-ranking and filtering strategies\n- Context window management\n- Evaluation metrics for retrieval\n\n### Principles\n\n- Retrieval quality > Generation quality - fix retrieval first\n- Chunk size depends on content type and query patterns\n- Embeddings are not magic - they have blind spots\n- Always evaluate retrieval separately from generation\n- Hybrid search beats pure semantic in most cases\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## Prerequisites\n\n- Required skills: LLM fundamentals, Understanding of embeddings, Basic NLP concepts\n\n## Patterns\n\n### Semantic Chunking\n\nChunk by meaning, not arbitrary token counts\n\n**When to use**: Processing documents with natural sections\n\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### Hierarchical Retrieval\n\nMulti-level retrieval for better precision\n\n**When to use**: Large document collections with varied granularity\n\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### Hybrid Search\n\nCombine semantic and keyword search\n\n**When to use**: Queries may be keyword-heavy or semantic\n\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### Query Expansion\n\nExpand queries to improve recall\n\n**When to use**: User queries are short or ambiguous\n\n- Use LLM to generate query variations\n- Add synonyms and related terms\n- Hypothetical Document Embedding (HyDE)\n- Multi-query retrieval with deduplication\n\n### Contextual Compression\n\nCompress retrieved context to fit window\n\n**When to use**: Retrieved chunks exceed context limits\n\n- Extract relevant sentences only\n- Use LLM to summarize chunks\n- Remove redundant information\n- Prioritize by relevance score\n\n### Metadata Filtering\n\nPre-filter by metadata before semantic search\n\n**When to use**: Documents have structured metadata\n\n- Filter by date, source, category first\n- Reduce search space before vector similarity\n- Combine metadata filters with semantic scores\n- Index metadata for fast filtering\n\n## Sharp Edges\n\n### Fixed-size chunking breaks sentences and context\n\nSeverity: HIGH\n\nSituation: Using fixed token\u002Fcharacter limits for chunking\n\nSymptoms:\n- Retrieved chunks feel incomplete or cut off\n- Answer quality varies wildly\n- High recall but low precision\n\nWhy this breaks:\nFixed-size chunks split mid-sentence, mid-paragraph, or mid-idea.\nThe resulting embeddings represent incomplete thoughts, leading to\npoor retrieval quality. Users search for concepts but get fragments.\n\nRecommended fix:\n\nUse semantic chunking that respects document structure:\n- Split on sentence\u002Fparagraph boundaries\n- Use embedding similarity to detect topic shifts\n- Include overlap for context continuity\n- Preserve headers and document structure as metadata\n\n### Pure semantic search without metadata pre-filtering\n\nSeverity: MEDIUM\n\nSituation: Only using vector similarity, ignoring metadata\n\nSymptoms:\n- Returns outdated information\n- Mixes content from wrong sources\n- Users can't scope their searches\n\nWhy this breaks:\nSemantic search finds semantically similar content, but not necessarily\nrelevant content. Without metadata filtering, you return old docs when\nuser wants recent, wrong categories, or inapplicable content.\n\nRecommended fix:\n\nImplement hybrid filtering:\n- Pre-filter by metadata (date, source, category) before vector search\n- Post-filter results by relevance criteria\n- Include metadata in the retrieval API\n- Allow users to specify filters\n\n### Using same embedding model for different content types\n\nSeverity: MEDIUM\n\nSituation: One embedding model for code, docs, and structured data\n\nSymptoms:\n- Code search returns irrelevant results\n- Domain terms not matched properly\n- Similar concepts not clustered\n\nWhy this breaks:\nEmbedding models are trained on specific content types. Using a text\nembedding model for code, or a general model for domain-specific\ncontent, produces poor similarity matches.\n\nRecommended fix:\n\nEvaluate embeddings per content type:\n- Use code-specific embeddings for code (e.g., CodeBERT)\n- Consider domain-specific or fine-tuned embeddings\n- Benchmark retrieval quality before choosing\n- Separate indices for different content types if needed\n\n### Using first-stage retrieval results directly\n\nSeverity: MEDIUM\n\nSituation: Taking top-K from vector search without reranking\n\nSymptoms:\n- Clearly relevant docs not in top results\n- Results order seems arbitrary\n- Adding more results helps quality\n\nWhy this breaks:\nFirst-stage retrieval (vector search) optimizes for recall, not precision.\nThe top results by embedding similarity may not be the most relevant\nfor the specific query. Cross-encoder reranking dramatically improves\nprecision for the final results.\n\nRecommended fix:\n\nAdd reranking step:\n- Retrieve larger candidate set (e.g., top 20-50)\n- Rerank with cross-encoder (query-document pairs)\n- Return reranked top-K (e.g., top 5)\n- Cache reranker for performance\n\n### Cramming maximum context into LLM prompt\n\nSeverity: MEDIUM\n\nSituation: Using all retrieved context regardless of relevance\n\nSymptoms:\n- Answers drift with more context\n- LLM ignores key information\n- High token costs\n\nWhy this breaks:\nMore context isn't always better. Irrelevant context confuses the LLM,\nincreases latency and cost, and can cause the model to ignore the\nmost relevant information. Models have attention limits.\n\nRecommended fix:\n\nUse relevance thresholds:\n- Set minimum similarity score cutoff\n- Limit context to truly relevant chunks\n- Summarize or compress if needed\n- Order context by relevance\n\n### Not measuring retrieval quality separately from generation\n\nSeverity: HIGH\n\nSituation: Only evaluating end-to-end RAG quality\n\nSymptoms:\n- Can't diagnose poor RAG performance\n- Prompt changes don't help\n- Random quality variations\n\nWhy this breaks:\nIf answers are wrong, you can't tell if retrieval failed or generation\nfailed. This makes debugging impossible and leads to wrong fixes\n(tuning prompts when retrieval is the problem).\n\nRecommended fix:\n\nSeparate retrieval evaluation:\n- Create retrieval test set with relevant docs labeled\n- Measure MRR, NDCG, Recall@K for retrieval\n- Evaluate generation only on correct retrievals\n- Track metrics over time\n\n### Not updating embeddings when source documents change\n\nSeverity: MEDIUM\n\nSituation: Embeddings generated once, never refreshed\n\nSymptoms:\n- Returns outdated information\n- References deleted content\n- Inconsistent with source\n\nWhy this breaks:\nDocuments change but embeddings don't. Users retrieve outdated content\nor, worse, content that no longer exists. This erodes trust in the\nsystem.\n\nRecommended fix:\n\nImplement embedding refresh:\n- Track document versions\u002Fhashes\n- Re-embed on document change\n- Handle deleted documents\n- Consider TTL for embeddings\n\n### Same retrieval strategy for all query types\n\nSeverity: MEDIUM\n\nSituation: Using pure semantic search for keyword-heavy queries\n\nSymptoms:\n- Exact term searches miss results\n- Concept searches too literal\n- Users frustrated with both\n\nWhy this breaks:\nSome queries are keyword-oriented (looking for specific terms) while\nothers are semantic (looking for concepts). Pure semantic search fails\non exact matches; pure keyword search fails on paraphrases.\n\nRecommended fix:\n\nImplement hybrid search:\n- BM25\u002FTF-IDF for keyword matching\n- Vector similarity for semantic matching\n- Reciprocal Rank Fusion to combine\n- Tune weights based on query patterns\n\n## Related Skills\n\nWorks well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend`\n\n## When to Use\n\n- User mentions or implies: building RAG\n- User mentions or implies: vector search\n- User mentions or implies: embeddings\n- User mentions or implies: semantic search\n- User mentions or implies: document retrieval\n- User mentions or implies: context retrieval\n- User mentions or implies: knowledge base\n- User mentions or implies: LLM with documents\n- User mentions or implies: chunking strategy\n- User mentions or implies: pinecone\n- User mentions or implies: weaviate\n- User mentions or implies: chromadb\n- User mentions or implies: pgvector\n","# RAG Engineer\n\nExpert in building Retrieval-Augmented Generation systems. Masters embedding models,\nvector databases, chunking strategies, and retrieval optimization for LLM applications.\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### Expertise\n\n- Embedding model selection and fine-tuning\n- Vector database architecture and scaling\n- Chunking strategies for different content types\n- Retrieval quality optimization\n- Hybrid search implementation\n- Re-ranking and filtering strategies\n- Context window management\n- Evaluation metrics for retrieval\n\n### Principles\n\n- Retrieval quality > Generation quality - fix retrieval first\n- Chunk size depends on content type and query patterns\n- Embeddings are not magic - they have blind spots\n- Always evaluate retrieval separately from generation\n- Hybrid search beats pure semantic in most cases\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## Prerequisites\n\n- Required skills: LLM fundamentals, Understanding of embeddings, Basic NLP concepts\n\n## Patterns\n\n### Semantic Chunking\n\nChunk by meaning, not arbitrary token counts\n\n**When to use**: Processing documents with natural sections\n\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### Hierarchical Retrieval\n\nMulti-level retrieval for better precision\n\n**When to use**: Large document collections with varied granularity\n\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### Hybrid Search\n\nCombine semantic and keyword search\n\n**When to use**: Queries may be keyword-heavy or semantic\n\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### Query Expansion\n\nExpand queries to improve recall\n\n**When to use**: User queries are short or ambiguous\n\n- Use LLM to generate query variations\n- Add synonyms and related terms\n- Hypothetical Document Embedding (HyDE)\n- Multi-query retrieval with deduplication\n\n### Contextual Compression\n\nCompress retrieved context to fit window\n\n**When to use**: Retrieved chunks exceed context limits\n\n- Extract relevant sentences only\n- Use LLM to summarize chunks\n- Remove redundant information\n- Prioritize by relevance score\n\n### Metadata Filtering\n\nPre-filter by metadata before semantic search\n\n**When to use**: Documents have structured metadata\n\n- Filter by date, source, category first\n- Reduce search space before vector similarity\n- Combine metadata filters with semantic scores\n- Index metadata for fast filtering\n\n## Sharp Edges\n\n### Fixed-size chunking breaks sentences and context\n\nSeverity: HIGH\n\nSituation: Using fixed token\u002Fcharacter limits for chunking\n\nSymptoms:\n- Retrieved chunks feel incomplete or cut off\n- Answer quality varies wildly\n- High recall but low precision\n\nWhy this breaks:\nFixed-size chunks split mid-sentence, mid-paragraph, or mid-idea.\nThe resulting embeddings represent incomplete thoughts, leading to\npoor retrieval quality. Users search for concepts but get fragments.\n\nRecommended fix:\n\nUse semantic chunking that respects document structure:\n- Split on sentence\u002Fparagraph boundaries\n- Use embedding similarity to detect topic shifts\n- Include overlap for context continuity\n- Preserve headers and document structure as metadata\n\n### Pure semantic search without metadata pre-filtering\n\nSeverity: MEDIUM\n\nSituation: Only using vector similarity, ignoring metadata\n\nSymptoms:\n- Returns outdated information\n- Mixes content from wrong sources\n- Users can't scope their searches\n\nWhy this breaks:\nSemantic search finds semantically similar content, but not necessarily\nrelevant content. Without metadata filtering, you return old docs when\nuser wants recent, wrong categories, or inapplicable content.\n\nRecommended fix:\n\nImplement hybrid filtering:\n- Pre-filter by metadata (date, source, category) before vector search\n- Post-filter results by relevance criteria\n- Include metadata in the retrieval API\n- Allow users to specify filters\n\n### Using same embedding model for different content types\n\nSeverity: MEDIUM\n\nSituation: One embedding model for code, docs, and structured data\n\nSymptoms:\n- Code search returns irrelevant results\n- Domain terms not matched properly\n- Similar concepts not clustered\n\nWhy this breaks:\nEmbedding models are trained on specific content types. Using a text\nembedding model for code, or a general model for domain-specific\ncontent, produces poor similarity matches.\n\nRecommended fix:\n\nEvaluate embeddings per content type:\n- Use code-specific embeddings for code (e.g., CodeBERT)\n- Consider domain-specific or fine-tuned embeddings\n- Benchmark retrieval quality before choosing\n- Separate indices for different content types if needed\n\n### Using first-stage retrieval results directly\n\nSeverity: MEDIUM\n\nSituation: Taking top-K from vector search without reranking\n\nSymptoms:\n- Clearly relevant docs not in top results\n- Results order seems arbitrary\n- Adding more results helps quality\n\nWhy this breaks:\nFirst-stage retrieval (vector search) optimizes for recall, not precision.\nThe top results by embedding similarity may not be the most relevant\nfor the specific query. Cross-encoder reranking dramatically improves\nprecision for the final results.\n\nRecommended fix:\n\nAdd reranking step:\n- Retrieve larger candidate set (e.g., top 20-50)\n- Rerank with cross-encoder (query-document pairs)\n- Return reranked top-K (e.g., top 5)\n- Cache reranker for performance\n\n### Cramming maximum context into LLM prompt\n\nSeverity: MEDIUM\n\nSituation: Using all retrieved context regardless of relevance\n\nSymptoms:\n- Answers drift with more context\n- LLM ignores key information\n- High token costs\n\nWhy this breaks:\nMore context isn't always better. Irrelevant context confuses the LLM,\nincreases latency and cost, and can cause the model to ignore the\nmost relevant information. Models have attention limits.\n\nRecommended fix:\n\nUse relevance thresholds:\n- Set minimum similarity score cutoff\n- Limit context to truly relevant chunks\n- Summarize or compress if needed\n- Order context by relevance\n\n### Not measuring retrieval quality separately from generation\n\nSeverity: HIGH\n\nSituation: Only evaluating end-to-end RAG quality\n\nSymptoms:\n- Can't diagnose poor RAG performance\n- Prompt changes don't help\n- Random quality variations\n\nWhy this breaks:\nIf answers are wrong, you can't tell if retrieval failed or generation\nfailed. This makes debugging impossible and leads to wrong fixes\n(tuning prompts when retrieval is the problem).\n\nRecommended fix:\n\nSeparate retrieval evaluation:\n- Create retrieval test set with relevant docs labeled\n- Measure MRR, NDCG, Recall@K for retrieval\n- Evaluate generation only on correct retrievals\n- Track metrics over time\n\n### Not updating embeddings when source documents change\n\nSeverity: MEDIUM\n\nSituation: Embeddings generated once, never refreshed\n\nSymptoms:\n- Returns outdated information\n- References deleted content\n- Inconsistent with source\n\nWhy this breaks:\nDocuments change but embeddings don't. Users retrieve outdated content\nor, worse, content that no longer exists. This erodes trust in the\nsystem.\n\nRecommended fix:\n\nImplement embedding refresh:\n- Track document versions\u002Fhashes\n- Re-embed on document change\n- Handle deleted documents\n- Consider TTL for embeddings\n\n### Same retrieval strategy for all query types\n\nSeverity: MEDIUM\n\nSituation: Using pure semantic search for keyword-heavy queries\n\nSymptoms:\n- Exact term searches miss results\n- Concept searches too literal\n- Users frustrated with both\n\nWhy this breaks:\nSome queries are keyword-oriented (looking for specific terms) while\nothers are semantic (looking for concepts). Pure semantic search fails\non exact matches; pure keyword search fails on paraphrases.\n\nRecommended fix:\n\nImplement hybrid search:\n- BM25\u002FTF-IDF for keyword matching\n- Vector similarity for semantic matching\n- Reciprocal Rank Fusion to combine\n- Tune weights based on query patterns\n\n## Related Skills\n\nWorks well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend`\n\n## When to Use\n\n- User mentions or implies: building RAG\n- User mentions or implies: vector search\n- User mentions or implies: embeddings\n- User mentions or implies: semantic search\n- User mentions or implies: document retrieval\n- User mentions or implies: context retrieval\n- User mentions or implies: knowledge base\n- User mentions or implies: LLM with documents\n- User mentions or implies: chunking strategy\n- User mentions or implies: pinecone\n- User mentions or implies: weaviate\n- User mentions or implies: chromadb\n- User mentions or implies: pgvector\n",{"name":5,"risk":34,"source":35,"date_added":36,"description":7},"unknown","vibeship-spawner-skills (Apache 2.0)",1772150400000,"main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Frag-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":46,"prompt":56,"zipAvailable":57},[47,50,53],{"tool":48,"command":49},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill rag-engineer",{"tool":51,"command":52},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill rag-engineer",{"tool":54,"command":55},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill rag-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\u002Frag-engineer\u002FSKILL.md",true,[],[60,73,90,117,136,162],{"skillNo":61,"name":62,"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":63,"categories":64,"keywords":70,"publishedAt":29},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[65],{"categoryNo":66,"categoryName":67,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[71,72],"Test Skill","test",{"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":29},"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",[],[80,81,82,83,84,85,86,87,88,89],"zustand store ts","zustand","store","with","types","middleware","patterns","community","create","typescript",{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":99,"publishedAt":29},"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",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000023","Skill与智能体开发",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,87,114,115,116],"automation","zoom automation","zoom","first","Tools","search","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":124,"publishedAt":29},"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",[123],{"categoryNo":97,"categoryName":98,"taxonomyType":68,"taxonomyTypeDesc":69},[125,100,126,127,128,129,130,87,105,131,113,132,110,109,104,133,88,134,106,135],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":145,"publishedAt":29},"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",[142],{"categoryNo":143,"categoryName":144,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000009","前端开发",[28,146,147,148,149,89,150,151,152,153,154,155,83,156,157,158,159,87,160,161],"zod","zod validation expert","validation","react","safe","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":169,"publishedAt":29},"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",[168],{"categoryNo":97,"categoryName":98,"taxonomyType":68,"taxonomyTypeDesc":69},[170,171,172,173,174,175,176,177,87,178,179,180,181,182,183,184,185,186,187,106],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","data","flow","verification","code","rust"]