[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EEKJKJCDNBE87V7F009W1Y":3,"skill-same:01M6EEKJKJCDNBE87V7F009W1Y":69},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":20,"frontMatter":21,"refName":22,"commitSha":23,"skillDirectory":24,"forksCount":25,"repositoryHtmlUrl":26,"repositoryHomepageUrl":27,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":29,"updatedAt":30,"install":31,"resources":44},"01M6EEKJKJCDNBE87V7F009W1Y","chromadb",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FTerminalSkills\u002Fskills\u002Fchromadb\u002FSKILL.md",[],[5],"2026-11-10 10:12:30",false,"# ChromaDB\n\n## Overview\n\nChromaDB is an open-source vector database for storing, searching, and managing embeddings. It provides a simple API for document ingestion, semantic similarity search, and metadata filtering, supporting both Python and JavaScript\u002FTypeScript clients with embedded, server, and cloud deployment options.\n\n## Instructions\n\n- When initializing, use `get_or_create_collection` for idempotent collection setup, choose `PersistentClient` for development and `HttpClient` for production server connections.\n- When adding documents, batch `add()` calls in chunks of 5,000 documents, always store source metadata (filename, URL, page number) for RAG citations, and use `upsert()` for incremental updates to avoid duplicates.\n- When querying, use `collection.query(query_texts=..., n_results=...)` for text-based search, combine metadata `where` filters to narrow results before semantic search, and set `n_results` based on the LLM's context window (5-10 for most RAG pipelines).\n- When choosing embeddings, use the default Sentence Transformers for local development without API keys, OpenAI or Cohere embedding functions for production, or pass pre-computed vectors directly.\n- When filtering metadata, use operators like `$eq`, `$gt`, `$in` with `$and`\u002F`$or` logical operators, and combine with `where_document` for content-based filtering alongside semantic similarity.\n- When deploying, use the embedded `PersistentClient` for single-node applications, Docker for server mode, or Chroma Cloud for managed hosting with multi-tenancy support.\n- When tuning performance, configure HNSW parameters (`hnsw:M`, `hnsw:construction_ef`, `hnsw:search_ef`) for the quality-speed tradeoff and choose `cosine` distance for normalized embeddings (OpenAI, Cohere).\n\n## Examples\n\n### Example 1: Build a document Q&A pipeline\n\n**User request:** \"Set up a RAG pipeline with ChromaDB for answering questions about our docs\"\n\n**Actions:**\n1. Load documents and split into chunks with metadata (source, page)\n2. Create a collection with OpenAI embedding function\n3. Batch-add document chunks with `upsert()` for idempotent ingestion\n4. Query with `collection.query()` and pass retrieved chunks as context to the LLM\n\n**Output:** A semantic search pipeline that retrieves relevant document chunks for LLM-powered Q&A.\n\n### Example 2: Add filtered semantic search to an application\n\n**User request:** \"Implement product search that combines text similarity with category filters\"\n\n**Actions:**\n1. Create a collection with product descriptions and category metadata\n2. Implement search combining `query_texts` with `where={\"category\": \"electronics\"}`\n3. Return results with distances for relevance ranking\n4. Add price range filtering with `$gte` and `$lte` operators\n\n**Output:** A filtered semantic search that narrows by metadata before ranking by text similarity.\n\n## Guidelines\n\n- Use `get_or_create_collection` for idempotent collection initialization; it is safe for restarts.\n- Batch `add()` calls in chunks of 5,000 documents to manage memory usage.\n- Always store source metadata (filename, URL, page number); it is essential for RAG citations.\n- Use `upsert()` for incremental updates to avoid duplicate documents when re-ingesting.\n- Set `n_results` based on the LLM's context window: 5-10 results for most RAG pipelines.\n- Use metadata filtering to narrow results before semantic search to reduce noise.\n- Choose `cosine` distance for normalized embeddings (OpenAI, Cohere) and `l2` for unnormalized.\n",{},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002FTerminalSkills\u002Fskills\u002Fchromadb",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":7,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill chromadb",{"tool":37,"command":38},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill chromadb",{"tool":40,"command":41},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill chromadb","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FTerminalSkills\u002Fskills\u002Fchromadb\u002FSKILL.md",true,[45,51,55,61,65],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"assets\u002Fmodule-help.csv","ASSET","资产文件",112,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.pi\u002Fskills\u002Fbmad-module-builder\u002Fassets\u002Fsetup-skill-template\u002Fassets\u002Fmodule-help.csv",{"path":52,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":53,"sourceHtmlUrl":54},"assets\u002Fmodule.yaml",96,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.pi\u002Fskills\u002Fbmad-module-builder\u002Fassets\u002Fsetup-skill-template\u002Fassets\u002Fmodule.yaml",{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"scripts\u002Fcleanup-legacy.py","SCRIPT","脚本",7743,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.pi\u002Fskills\u002Fbmad-module-builder\u002Fassets\u002Fsetup-skill-template\u002Fscripts\u002Fcleanup-legacy.py",{"path":62,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":63,"sourceHtmlUrl":64},"scripts\u002Fmerge-config.py",14139,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.pi\u002Fskills\u002Fbmad-module-builder\u002Fassets\u002Fsetup-skill-template\u002Fscripts\u002Fmerge-config.py",{"path":66,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":67,"sourceHtmlUrl":68},"scripts\u002Fmerge-help-csv.py",6607,"https:\u002F\u002Fgithub.com\u002FdevelopAgent\u002Fbmad-start\u002Fblob\u002Fmain\u002F.pi\u002Fskills\u002Fbmad-module-builder\u002Fassets\u002Fsetup-skill-template\u002Fscripts\u002Fmerge-help-csv.py",[70,84,94,103,109,116],{"skillNo":71,"name":72,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":80,"publishedAt":18},"01M6FKJN13GMG4KY57J3QD8GY5","model-hierarchy-skill","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzscole\u002Fmodel-hierarchy-skill\u002Fmodel-hierarchy-skill\u002FSKILL.md",[75],{"categoryNo":76,"categoryName":77,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[81,82,83],"model","model hierarchy","model hierarchy skill",{"skillNo":85,"name":86,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":90,"publishedAt":18},"01M6FKJJJ6GCAMR4PPPRD12M5X","zoom-mcp","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-mcp\u002FSKILL.md",[89],{"categoryNo":76,"categoryName":77,"taxonomyType":78,"taxonomyTypeDesc":79},[91,92,93],"MCP","zoom","zoom mcp",{"skillNo":95,"name":96,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":18},"01M6FKJE3KYT3KPEZMEEHYKA6K","zoom-apps-sdk","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[100,101,102,92],"sdk","zoom apps sdk","apps",{"skillNo":104,"name":105,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":18},"01M6FKJ9EDH9Q5VWDF8HAJRSP0","windows","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwindows\u002FSKILL.md",[],[105],{"skillNo":110,"name":111,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":115,"publishedAt":18},"01M6FKJ666BEEFC3AE9GJ72Q93","whiteboard","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwhiteboard\u002FSKILL.md",[114],{"categoryNo":76,"categoryName":77,"taxonomyType":78,"taxonomyTypeDesc":79},[111],{"skillNo":117,"name":118,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":18},"01M6FKJ21WFCDGTJQ2Q91T9DVW","websockets","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwebsockets\u002FSKILL.md",[],[118]]