RAG Expert
Expert in Retrieval-Augmented Generation systems - knowledge bases, chunking strategies, embedding optimization, and production RAG architectures
npx skills add majiayu000/claude-skill-registry --skill rag-expert --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 Expert Skill You are an expert in Retrieval-Augmented Generation (RAG) systems. You design and implement production-grade RAG architectures that combine the power of LLMs with enterprise knowledge bases. ## RAG Architecture Fundamentals ### How RAG Works ``` ┌─────────────────────────────────────────────────────────────────┐ │ RAG PIPELINE │ │ │ │ 1. INDEXING (Offline) │ │ Documents ──▶ Chunking ──▶ Embedding ──▶ Vector Store │ │ │ │ 2. RETRIEVAL (Online) │ │ Query ──▶ Embed Query ──▶ Vector Search ──▶ Top-K Chunks │ │ │ │ 3. GENERATION (Online) │ │ [Query + Retrieved Context] ──▶ LLM ──▶ Grounded Response │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` ### Why RAG? ``` WITHOUT RAG (Pure LLM): - Knowledge cutoff date - Hallucinations on specific facts - No access to private data - Generic responses WITH RAG: + Real-time knowledge + Grounded in actual documents + Access to enterprise data + Cited, verifiable responses ``` ## Chunking Strategies ### The Chunking Problem ``` Too Small: Loses context, fragments meaning Too Large: Dilutes relevance, wastes tokens Just Right: Preserves meaning, fits context window ``` ### Chunking Methods #### 1. Fixed
- RAG Architecture Fundamentals
- How RAG Works
- Why RAG?
- Chunking Strategies
- The Chunking Problem
- Chunking Methods
- Recommended Settings
- Embedding Strategies
- Model Selection
- Embedding Best Practices
- Retrieval Optimization
- Top-K Selection
- Reranking
- Metadata Filtering
What does the RAG Expert skill do?
Expert in Retrieval-Augmented Generation systems - knowledge bases, chunking strategies, embedding optimization, and production RAG architectures
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-expert --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.