ai-rag
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.
npx skills add majiayu000/claude-skill-registry --skill ai-rag --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 & Search Engineering — Complete Reference Build production-grade retrieval systems with **hybrid search**, **grounded generation**, and **measurable quality**. This skill covers: - **RAG**: Chunking, contextual retrieval, grounding, adaptive/self-correcting systems - **Search**: BM25, vector search, hybrid fusion, ranking pipelines - **Evaluation**: recall@k, nDCG, MRR, groundedness metrics **Modern Best Practices (December 2025)**: - Separate **retrieval quality** from **answer quality**; evaluate both (RAG paper: https://arxiv.org/abs/2005.11401). - Default to **hybrid retrieval** (sparse + dense) with **reranking** when precision matters (DPR: https://arxiv.org/abs/2004.04906). - Treat **freshness/invalidation** as first-class; staleness is a correctness bug, not a UX issue. - Add **grounding gates**: answerability checks, citation coverage checks, and refusal-on-missing-context defaults. - Threat-model RAG: retrieved text is untrusted input (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/). **Default posture**: deterministic pipeline, bounded context, explicit failure handling, and telemetry for every stage. **Scope note**: Fo
- Quick Reference
- Decision Tree: RAG Architecture Selection
- Core Concepts (Vendor-Agnostic)
- Implementation Practices (Tooling Examples)
- Do / Avoid
- When to Use This Skill
- Related Skills
- Detailed Guides
- Core RAG Architecture
- Advanced Retrieval Techniques
- Production & Evaluation
- Existing Detailed Patterns
- Templates
- System Design (Start Here)
What does the ai-rag skill do?
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-rag --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.