rag-system-builder
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
npx skills add majiayu000/claude-skill-registry --skill rag-system-builder --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 System Builder Skill ## Overview This skill creates complete RAG (Retrieval-Augmented Generation) systems that combine semantic search with LLM-powered Q&A. Users can ask natural language questions and receive accurate answers grounded in your document collection. ## Quick Start ```python from sentence_transformers import SentenceTransformer import anthropic # Setup model = SentenceTransformer('all-MiniLM-L6-v2') client = anthropic.Anthropic() # Retrieve context (simplified) query = "What are the safety requirements?" query_embedding = model.encode(query, normalize_embeddings=True) # ... search for similar chunks ... # Generate answer response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[{"role": "user", "content": f"Context: {context}\n\nQuestion: {query}"}] ) print(response.content[0].text) ``` ## When to Use - Building AI assistants for technical documentation - Creating Q&A systems for standards libraries - Developing chatbots with domain expertise - Enabling natural language queries over knowledge bases - Adding AI-powered search to existing document systems ## Architecture ``` User Question | v +------------------+ | 1. Embed Qu
- Overview
- Quick Start
- When to Use
- Architecture
- Prerequisites
- Implementation
- Step 1: Vector Embeddings Table
- Step 2: Generate Embeddings
- Step 3: Semantic Search
- Step 4: RAG Query Engine
- Step 5: CLI Interface
- Prompt Engineering Tips
- System Prompt Template
- Multi-Turn Conversations
Single question Interactive mode With OpenAI pip install sentence-transformers anthropic openai numpy
What does the rag-system-builder skill do?
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-system-builder --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.