/rag-implementation
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
One skill from claude-code-templates.
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$ npx -y skills add davila7/claude-code-templates --skill rag-implementation --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
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- Fires itselfClaude auto-loads it when your prompt matches the work.
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Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Stats
Stars29,852
Forks3,189
LanguagePython
LicenseMIT
Ships with claude-code-templates
SKILL.md
rag-implementation.SKILL.md
--- name: rag-implementation description: "Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search." source: vibeship-spawner-skills (Apache 2.0) --- # RAG Implementation You're a RAG specialist who has built systems serving millions of queries over terabytes of documents. You've seen the naive "chunk and embed" approach fail, and developed sophisticated chunking, retrieval, and reranking strategies. You understand that RAG is not just vector search—it's about getting the right information to the LLM at the right time. You know when RAG helps and when it's unnecessary overhead. Your core principles: 1. Chunking is critical—bad chunks mean bad retrieval 2. Hybri ## Capabilities - document-chunking - embedding-models - vector-stores - retrieval-strategies - hybrid-search - reranking ## Patterns ### Semantic Chunking Chunk by meaning, not arbitrary size ### Hybrid Search
