rag-first — for Claude Code rag-first, dev-rag, community, for Claude Code, ide skills, mcp__devrag__search, Codebase, Search, Before, Explore

v1.0.0

À propos de ce Skill

Scenario recommande : Ideal for AI agents that need rag-first codebase search. Resume localise : # RAG-First Codebase Search Before using Grep, Glob, Explore agents, or other codebase exploration tools, ALWAYS search DevRAG first. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Fonctionnalités

RAG-First Codebase Search
Call mcp devrag search with your query.
Evaluate the results:
For "where is X defined?" questions, RAG's AST-aware code chunks often give better results than
If the DevRAG MCP server is not available (tool call fails), fall back to direct exploration

# Sujets clés

tomharris tomharris
[1]
[0]
Mis à jour: 4/17/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Scenario recommande : Ideal for AI agents that need rag-first codebase search. Resume localise : # RAG-First Codebase Search Before using Grep, Glob, Explore agents, or other codebase exploration tools, ALWAYS search DevRAG first. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Pourquoi utiliser cette compétence

Recommandation : rag-first helps agents rag-first codebase search. RAG-First Codebase Search Before using Grep, Glob, Explore agents, or other codebase exploration tools, ALWAYS search DevRAG first. This AI agent skill

Meilleur pour

Scenario recommande : Ideal for AI agents that need rag-first codebase search.

Cas d'utilisation exploitables for rag-first

Cas d'usage : Applying RAG-First Codebase Search
Cas d'usage : Applying Call mcp devrag search with your query
Cas d'usage : Applying Evaluate the results:

! Sécurité et Limitations

  • Limitation : Requires repository-specific context from the skill documentation
  • Limitation : Works best when the underlying tools and dependencies are already configured

About The Source

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Démo Labs

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ et étapes d’installation

These questions and steps mirror the structured data on this page for better search understanding.

? Questions fréquentes

Qu’est-ce que rag-first ?

Scenario recommande : Ideal for AI agents that need rag-first codebase search. Resume localise : # RAG-First Codebase Search Before using Grep, Glob, Explore agents, or other codebase exploration tools, ALWAYS search DevRAG first. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Comment installer rag-first ?

Exécutez la commande : npx killer-skills add tomharris/dev-rag. Elle fonctionne avec Cursor, Windsurf, VS Code, Claude Code et plus de 19 autres IDE.

Quels sont les cas d’usage de rag-first ?

Les principaux cas d’usage incluent : Cas d'usage : Applying RAG-First Codebase Search, Cas d'usage : Applying Call mcp devrag search with your query, Cas d'usage : Applying Evaluate the results:.

Quels IDE sont compatibles avec rag-first ?

Cette skill est compatible avec Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Utilisez la CLI Killer-Skills pour une installation unifiée.

Y a-t-il des limites pour rag-first ?

Limitation : Requires repository-specific context from the skill documentation. Limitation : Works best when the underlying tools and dependencies are already configured.

Comment installer ce skill

  1. 1. Ouvrir le terminal

    Ouvrez le terminal ou la ligne de commande dans le dossier du projet.

  2. 2. Lancer la commande d’installation

    Exécutez : npx killer-skills add tomharris/dev-rag. La CLI détectera automatiquement votre IDE ou votre agent et configurera la skill.

  3. 3. Commencer à utiliser le skill

    Le skill est maintenant actif. Votre agent IA peut utiliser rag-first immédiatement dans le projet.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

rag-first

# RAG-First Codebase Search Before using Grep, Glob, Explore agents, or other codebase exploration tools, ALWAYS search DevRAG first. This AI agent skill

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

RAG-First Codebase Search

Before using Grep, Glob, Explore agents, or other codebase exploration tools, ALWAYS search DevRAG first. The RAG index has semantic understanding of code structure, PR history, and documentation that keyword search misses.

Process

  1. Formulate a search query from the user's question. Use natural language — DevRAG uses semantic search, not keyword matching. Include key terms but phrase it as a question or description.

  2. Call mcp__devrag__search with your query.

  3. Evaluate the results:

    • If results are relevant and sufficient — present them grouped by source type (code, PR, doc). Show file paths, snippets, PR numbers, and document sections.
    • If results are partial — use them as a starting point, then supplement with targeted Grep/Glob/Read on specific files or patterns identified from the RAG results.
    • If results are empty or irrelevant — state "RAG results were limited for this query, falling back to direct codebase exploration" and proceed with Grep/Glob/Read/Explore as normal.
  4. Combine sources — when RAG gives you file paths and context, use Read to pull in the full current code. RAG results may be from a previous index, so always verify against current files.

Key Guidelines

  • DevRAG searches across four collections: code chunks, PR diffs, PR discussions, and documents. A single query searches all relevant collections.
  • For "why did this change?" questions, RAG is especially powerful — it has PR history and review comments that Grep cannot find.
  • For "where is X defined?" questions, RAG's AST-aware code chunks often give better results than grep patterns.
  • Do NOT skip RAG even for seemingly simple lookups — the semantic search may surface related context you wouldn't have found with keywords.
  • If the DevRAG MCP server is not available (tool call fails), fall back to direct exploration without retrying.

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