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OpenAI · Retrieval-augmented generation
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects. | Orchestra-Research/ | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | 3 mo ago |
| 2 | Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search. | decolua/ | 31k | — | ~604 | Automated safety check: Pass | MIT | 3 days ago |
| 3 | Guide to building Node.js and TypeScript apps on the agent-squad package: orchestrator, agent types, classifier routing, storage, retrievers and MCP tools. | 2FastLabs/ | 7.8k | — | ~4.3k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 4 | Search 2500+ curated ChatGPT and LLM open-source repositories. | taishi-i/ | 3.3k | — | ~3.8k | Automated safety check: Pass | CC0-1.0 | 2 days ago |
| 5 | Guide for using the NeuroLink SDK and CLI. An agent skill from juspay/neurolink. | juspay/ | 148 | — | ~1.4k | Automated safety check: Pass | MIT | today |
| 6 | Helps choose and tune embedding models for semantic search and RAG: model comparison, chunking, preprocessing, normalization and caching. | wshobson/ | 40k | 10 repos | ~710 | Automated safety check: Pass | MIT | 6 days ago |
| 7 | Framework for building LLM-powered applications with agents, chains, and RAG. | Orchestra-Research/ | 13k | 2 repos | ~3.2k | Automated safety check: Pass | MIT | 3 mo ago |
| 8 | 千问AI平台(Qianwen AI Platform / DashScope)官方文档离线知识库,用于检索并回答模型选择、API Key、OpenAI 兼容接口、DashScope SDK、文本与多模态生成、图像/视频/语音、Realtime API、Embedding、Reranking、Function Calling、MCP、批量调用、计费、Token Plan、API/SDK/CLI… | chujianyun/ | 742 | — | ~717 | Automated safety check: Pass | Unknown | 2 days ago |
| 9 | Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step. | Jeffallan/ | 12k | — | ~2k | Automated safety check: Pass | MIT | 7 days ago |
| 10 | 10.AI Engineer A skill your agent uses when building production LLM applications — designing RAG pipelines, choosing vector databases, implementing agent orchestration, optimizing cost, or adding AI safety… | kid-sid/ | 190 | — | ~3.7k | Automated safety check: Pass | MIT | 2 mo ago |
| 11 | 当系统提示需要设计引用格式、信息溯源机制、来源标注系统时调用。适用于文档问答、搜索增强生成(RAG)、代码引用、浏览器辅助等需要让用户追溯信息来源的场景。不适用于纯创作类输出(如故事、诗歌),不适用于无需溯源的常识问答,也不适用于注入防御(虽然两者都涉及内容可信度)。 | kangarooking/ | 208 | — | ~655 | Automated safety check: Pass | MIT | 5 mo ago |
| 12 | Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and… | redis/ | 166 | — | ~1k | Automated safety check: Pass | MIT | 1 mo ago |
| 13 | INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. | langchain-ai/ | 1.3k | — | ~3.9k | Automated safety check: Pass | MIT | 2 days ago |
| 14 | Vector database implementation for AI/ML applications, semantic search, and RAG systems. | ancoleman/ | 525 | — | ~3.5k | Automated safety check: Pass | MIT | 10 mo ago |
| 15 | Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. | microsoft/ | 77k | — | ~1.3k | Automated safety check: Pass | MIT | 2 days ago |
| 16 | Build local-first AI agents wey dey run fully for developer workstation wit Microsoft Foundry Local and Qwen function-calling models. | microsoft/ | 77k | — | ~1.4k | Automated safety check: Pass | MIT | 2 days ago |
| 17 | A skill your agent uses for Azure AI: Search, Speech, OpenAI, Document Intelligence. | microsoft/ | 255 | 1 repo | ~852 | Automated safety check: Pass | MIT | yesterday |
| 18 | Bumuo ng mga local-first AI agents na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry Local at Qwen function-calling models. | microsoft/ | 77k | — | ~1.7k | Automated safety check: Pass | MIT | 2 days ago |
| 19 | A skill your agent uses when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. | rrezartprebreza/ | 301 | — | ~2.1k | Automated safety check: Pass | MIT | 20 days ago |
| 20 | A skill your agent uses when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. | rrezartprebreza/ | 301 | — | ~2.5k | Automated safety check: Pass | MIT | 20 days ago |
| 21 | 21.Local RAG 本地向量知识库,支持按项目管理文档(docx/doc/pdf/md),语义检索。默认用硅基流动免费 API,零模型安装即可使用。支持多项目隔离、中文制度文档专用切片、Embedding+Rerank 两阶段检索。触发词:知识库、向量检索、RAG、制度检索、文档入库、语义搜索、local… | nigo81/ | 133 | — | ~1.4k | Automated safety check: Pass | MIT | 1 mo ago |
| 22 | 22.Markitdown Convert files and Office documents into clean Markdown when you need LLM-friendly, token-efficient text (e.g., for summarization, search, RAG ingestion, or dataset preparation). | aipoch/ | 1.9k | — | ~1.3k | Automated safety check: Pass | MIT | 24 days ago |
| 23 | A skill your agent uses when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server —… | ericrisco/ | 180 | — | ~5k | Automated safety check: Pass | MIT | yesterday |
| 24 | A skill your agent uses whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots… | jeremylongshore/ | 2.8k | — | ~4k | Automated safety check: Notes | MIT | yesterday |
| 25 | Expert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart… | LeoYeAI/ | 2.2k | — | ~4.1k | Automated safety check: Notes | MIT | 2 mo ago |