RAG Implementer
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
npx skills add majiayu000/claude-skill-registry --skill rag-implementer --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 Implementer Build production-ready retrieval-augmented generation systems. ## Core Principle **RAG = Retrieval + Context Assembly + Generation** Use RAG when you need LLMs to access fresh, domain-specific, or proprietary knowledge that wasn't in their training data. --- ## ⚠️ Prerequisites & Cost Reality Check ### STOP: Have You Validated the Need for RAG? **Before implementing RAG, confirm:** - [ ] **Problem validated** - Completed `product-strategist` Phase 1 (problem discovery) - [ ] **Users need AI search** - Tested with simpler alternatives (see below) - [ ] **ROI justified** - Calculated cost vs benefit of RAG vs alternatives ### Try These FIRST (Before RAG) RAG is powerful but expensive. Try cheaper alternatives first: **1. FAQ Page / Documentation (1 day, $0)** - Create well-organized FAQ or docs - Add search with Cmd+F - **Works for:** <50 common questions, static content - **Test:** Do users find answers? If yes, stop here. **2. Simple Keyword Search (2-3 days, $0-20/month)** - Use Algolia, Typesense, or PostgreSQL full-text search - Good enough for 80% of use cases - **Works for:** <100k documents, keyword matching sufficient - **Test:** Do users get relevant resul
- Core Principle
- ⚠️ Prerequisites & Cost Reality Check
- STOP: Have You Validated the Need for RAG?
- Try These FIRST (Before RAG)
- Cost Reality Check
- Decision Tree: Do You Really Need RAG?
- Validation Checklist
- 8-Phase RAG Implementation
- Phase 1: Knowledge Base Design
- Phase 2: Embedding Strategy
- Phase 3: Vector Store Architecture
- Phase 4: Retrieval Pipeline
- Phase 5: Context Assembly
- Phase 6: Evaluation & Metrics
What does the RAG Implementer skill do?
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-implementer --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.