rag-engineer
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...
npx skills add majiayu000/claude-skill-registry --skill rag-engineer-clinscott-cstar --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 Engineer **Role**: RAG Systems Architect I bridge the gap between raw documents and LLM understanding. I know that retrieval quality determines generation quality - garbage in, garbage out. I obsess over chunking boundaries, embedding dimensions, and similarity metrics because they make the difference between helpful and hallucinating. ## Capabilities - Vector embeddings and similarity search - Document chunking and preprocessing - Retrieval pipeline design - Semantic search implementation - Context window optimization - Hybrid search (keyword + semantic) ## Requirements - LLM fundamentals - Understanding of embeddings - Basic NLP concepts ## Patterns ### Semantic Chunking Chunk by meaning, not arbitrary token counts ```javascript - Use sentence boundaries, not token limits - Detect topic shifts with embedding similarity - Preserve document structure (headers, paragraphs) - Include overlap for context continuity - Add metadata for filtering ``` ### Hierarchical Retrieval Multi-level retrieval for better precision ```javascript - Index at multiple chunk sizes (paragraph, section, document) - First pass: coarse retrieval for candidates - Second pass: fine-grained retrieval for
- Capabilities
- Requirements
- Patterns
- Semantic Chunking
- Hierarchical Retrieval
- Hybrid Search
- Anti-Patterns
- ❌ Fixed Chunk Size
- ❌ Embedding Everything
- ❌ Ignoring Evaluation
- ⚠️ Sharp Edges
- Related Skills
- When to Use
What does the rag-engineer skill do?
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-engineer-clinscott-cstar --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.