rag-engineer
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.
npx skills add majiayu000/claude-skill-registry --skill rag-engineer-sickn33-antigravity-awesome --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 Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. **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. ### Expertise - Embedding model selection and fine-tuning - Vector database architecture and scaling - Chunking strategies for different content types - Retrieval quality optimization - Hybrid search implementation - Re-ranking and filtering strategies - Context window management - Evaluation metrics for retrieval ### Principles - Retrieval quality > Generation quality - fix retrieval first - Chunk size depends on content type and query patterns - Embeddings are not magic - they have blind spots - Always evaluate retrieval separately from generation - Hybrid search beats pure semantic in most cases ## Capabilities - Vector embeddings and similarity search - Document chunking
- Expertise
- Principles
- Capabilities
- Prerequisites
- Patterns
- Semantic Chunking
- Hierarchical Retrieval
- Hybrid Search
- Query Expansion
- Contextual Compression
- Metadata Filtering
- Sharp Edges
- Fixed-size chunking breaks sentences and context
- Pure semantic search without metadata pre-filtering
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-engineer-sickn33-antigravity-awesome --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.