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davila7/claude-code-templates496 installs

rag-implementation

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

How do I install this agent skill?

npx skills add https://github.com/davila7/claude-code-templates --skill rag-implementation
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    No security issues were detected. The skill contains only instructional documentation for RAG implementation and does not include any executable code, tools, or network operations.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • Runlayerwarn

    1/1 file flagged

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

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:

  1. Chunking is critical—bad chunks mean bad retrieval
  2. 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

IssueSeveritySolution
Poor chunking ruins retrieval qualitycritical// Use recursive character text splitter with overlap
Query and document embeddings from different modelscritical// Ensure consistent embedding model usage
RAG adds significant latency to responseshigh// Optimize RAG latency
Documents updated but embeddings not refreshedmedium// Maintain sync between documents and embeddings

Related Skills

Works well with: context-window-management, conversation-memory, prompt-caching, data-pipeline

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/davila7/claude-code-templates/rag-implementation">View rag-implementation on skillZs</a>