10 actions in 8 pieces of software · ranked by words, meaning and measured trust · 676 ms

1. 1langchainverified and signedpassport

   skilldavila7/claude-code-templatesrecord

   • langchain

     Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agent

2. 2langchain4j-rag-implementation-patternsverified and signedpassport

   skillgiuseppe-trisciuoglio/developer-kitrecord

   • langchain4j-rag-implementation-patterns

     Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistan

3. 3langchain-ragdemo or tutorialno passport yet

   skilllangchain-ai/langchain-skillsrecord

   • langchain-rag

     INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

4. 4LangChain Agent RAG MCPfound in code, not yet checkedno passport yet
   • knowledge_base_search

     Search the project knowledge base built from README and agent notes.

   • project_summary

     Summarize the current project status, phase, and environment.

5. 5langchain-patternsverified and signedpassport

   skillvuralserhat86/antigravity-agentic-skillsrecord

   • langchain-patterns

     Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowl

6. 6building-rag-systemsverified and signedpassport

   skillpanaversity/agentfactoryrecord

   • building-rag-systems

     Build production RAG systems with LangChain orchestration and Qdrant vector store. Covers 8 RAG architectures (Simple, HyDE, CRAG, Self-RAG, Agentic), document processing, semantic chunking, retrieval chains, and evaluation with LangSmith/RAGAS. Use when implementing RAG pipelines, semantic search,

7. 7llm_rag_langchainfound in code, not yet checkedno passport yet

   MCP serverwontaeleeterry/llm_rag_langchain

   • retrieve_context

     사용자 질문(query)에 대해 벡터 유사도 기반 상위 context를 반환합니다. Claude는 반환된 context를 기반으로 최종 답변을 생성합니다.

   • recommend_menu

     Recommend a menu item based on user preference.

8. 8ai-documentation-assistant___agentic-rag-and-mcp-retrieval-assistantfound in code, not yet checkedno passport yet

   MCP serverahmed-maher77/ai-documentation-assistant___agentic-rag-and-mcp-retrieval-assistant

   • ask_documentation_assistant

     Query the full LangChain RAG agent with a question to get a synthesized answer based on documentation.