rag-systems
Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and grounding LLM responses in factual data.
npx skills add majiayu000/claude-skill-registry --skill rag-systems --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 Systems Build Retrieval-Augmented Generation systems that enhance LLMs with external knowledge. ## Overview RAG combines information retrieval with language generation, enabling LLMs to access and incorporate external knowledge for more accurate, up-to-date responses. ## Quick Reference | Scenario | Recommended Approach | Reference File | |----------|---------------------|----------------| | Document ingestion and vectorization | Chunking, embedding, indexing | `/references/data-ingestion.md` | | Finding relevant information | Retrieval strategies and ranking | `/references/retrieval-methods.md` | | Combining retrieval with generation | Prompt augmentation and generation | `/references/generation-techniques.md` | ## Core Principles 1. **Chunking** - Split documents into semantically coherent pieces 2. **Embedding** - Convert text to dense vector representations 3. **Indexing** - Store vectors for efficient similarity search 4. **Retrieval** - Find most relevant chunks for query 5. **Augmentation** - Inject retrieved context into LLM prompt ## RAG Pipeline ### 1. Data Ingestion - Load documents from various sources - Split into chunks (200-500 tokens typical) - Generate embedd
- Overview
- Quick Reference
- Core Principles
- RAG Pipeline
- 1. Data Ingestion
- 2. Retrieval
- 3. Generation
- Key Components
- Using the Reference Files
- Best Practices
- Common Pitfalls to Avoid
What does the rag-systems skill do?
Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and grounding LLM responses in factual data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-systems --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.