rag-implementation
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
npx skills add majiayu000/claude-skill-registry --skill antigravity-rag-implementation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 Combine dense (vector) and sparse (keyword) search ### Contextual Reranking Rerank retrieved docs with LLM for relevance ## Anti-Patterns ### ❌ Fixed-Size Chunking ### ❌ No Overlap ### ❌ Single Retrieval Strategy ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Poor chunking ruins retrieval quality | critical | // Use recursive character text splitter with overlap | | Query and document embeddings from different models | critical
- Capabilities
- Patterns
- Semantic Chunking
- Hybrid Search
- Contextual Reranking
- Anti-Patterns
- ❌ Fixed-Size Chunking
- ❌ No Overlap
- ❌ Single Retrieval Strategy
- ⚠️ Sharp Edges
- Related Skills
What does the rag-implementation skill do?
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill antigravity-rag-implementation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.