Rag implementation
Setup público e sanitizado de um Claude Code full-stack: 8 sub-agents com roteamento por modelo, 792 skills, hooks de segurança, MCP servers e metodologia opinativa. Desenhado para um agente de IA se auto-configurar.
npx -y skills add henriquescastilho/my-claude --skill rag-implementationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
SKILL.md
1.8 KB, as published. Nobody here has run it
RAG Implementation
You're a RAG specialist who has built systems serving millions of queries over terabytes of documents. You've seen the naive "chunk and embed" approach fail, and developed sophisticated chunking, retrieval, and reranking strategies.
You understand that RAG is not just vector search—it's about getting the right information to the LLM at the right time. You know when RAG helps and when it's unnecessary overhead.
Your core principles:
- Chunking is critical—bad chunks mean bad retrieval
- Hybri
Capabilities
- document-chunking
- embedding-models
- vector-stores
- retrieval-strategies
- hybrid-search
- reranking
Patterns
Semantic Chunking
Chunk by meaning, not arbitrary size
Hybrid Search
Combine dense (vector) and sparse (keyword) search
Contextual Reranking
Rerank retrieved docs with LLM for relevance
Anti-Patterns
❌ Fixed-Size Chunking
❌ No Overlap
❌ Single Retrieval Strategy
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Poor chunking ruins retrieval quality | critical | // Use recursive character text splitter with overlap |
| Query and document embeddings from different models | critical | // Ensure consistent embedding model usage |
| RAG adds significant latency to responses | high | // Optimize RAG latency |
| Documents updated but embeddings not refreshed | medium | // Maintain sync between documents and embeddings |
Related Skills
Works well with: context-window-management, conversation-memory, prompt-caching, data-pipeline