rag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
npx skills add majiayu000/claude-skill-registry --skill rag-giuseppe-trisciuogli-developer-kit --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 Implementation Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources. ## Overview This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation. ## When to Use - Building Q&A systems over proprietary documents - Creating chatbots with factual information from knowledge bases - Implementing semantic search with natural language queries - Reducing hallucinations with grounded, sourced responses - Building documentation assistants and research tools - Enabling AI systems to access domain-specific knowledge ## Instructions ### Step 1: Choose Vector Database Select based on your requirements: | Requirement | Recommended | |-------------|-------------| | Production scalability | Pinecone, Milvus | | Open-source | Weaviate, Qdrant | | Local development | Chroma, FAISS | | Hybrid search | Weaviate with BM25 | ### Step 2: Select Embedding Model | Use Case | Model | |----------|-------| | General purpose | text-embedding-ada-002 | | Fast and lightweight | all-MiniLM-L6-v2 | | Multilingual | e5-large-v2 | | Best performance | bge-large-en-v1.5 | ### Step 3: Impleme
- Overview
- When to Use
- Instructions
- Step 1: Choose Vector Database
- Step 2: Select Embedding Model
- Step 3: Implement Document Processing Pipeline
- Step 4: Configure Retrieval Strategy
- Step 5: Build RAG Pipeline
- Step 6: Evaluate and Optimize
- Examples
- Example 1: Basic Document Q&A
- Example 2: Metadata-Filtered Retrieval
- Example 3: Multi-Source RAG Pipeline
- Example 4: RAG with Chat Memory
What does the rag skill do?
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-giuseppe-trisciuogli-developer-kit --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.