RAG Pipeline Builder
Build retrieval-augmented generation systems that ground LLM responses in your data
npx skills add majiayu000/claude-skill-registry --skill rag-pipeline-builder-eddiebe147-claude-settings --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 Pipeline Builder The RAG Pipeline Builder skill guides you through designing and implementing Retrieval-Augmented Generation systems that enhance LLM responses with relevant context from your own data. RAG combines the power of large language models with the precision of information retrieval, reducing hallucinations and enabling AI to work with private, current, or domain-specific knowledge. This skill covers the complete RAG stack: document ingestion, chunking strategies, embedding generation, vector storage, retrieval optimization, context injection, and response generation. It helps you make informed decisions at each stage based on your specific requirements for accuracy, latency, cost, and scale. Whether you are building a documentation Q&A bot, a customer support system, or an enterprise knowledge assistant, this skill ensures your RAG implementation follows production best practices. ## Core Workflows ### Workflow 1: Design RAG Architecture 1. **Define** requirements: - Data sources and formats - Query types and patterns - Accuracy requirements - Latency budget - Scale expectations 2. **Choose** components: - Document loaders - Chunking strategy - Embedding model - Ve
- Core Workflows
- Workflow 1: Design RAG Architecture
- Workflow 2: Implement Ingestion Pipeline
- Workflow 3: Optimize Retrieval Quality
- Quick Reference
- Best Practices
- Advanced Techniques
- Multi-Index Strategy
- Query Transformation Pipeline
- Contextual Compression
- Agentic RAG
- Evaluation Framework
- Common Pitfalls to Avoid
What does the RAG Pipeline Builder skill do?
Build retrieval-augmented generation systems that ground LLM responses in your data
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-pipeline-builder-eddiebe147-claude-settings --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.