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

Über diesen Skill

Geeigneter Einsatz: Ideal for AI agents that need rag-first codebase search. Lokalisierte Zusammenfassung: # 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.

Funktionen

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

# Kernthemen

tomharris tomharris
[1]
[0]
Aktualisiert: 4/17/2026

Skill Overview

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

Geeigneter Einsatz: Ideal for AI agents that need rag-first codebase search. Lokalisierte Zusammenfassung: # 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.

Warum diese Fähigkeit verwenden

Empfehlung: 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

Am besten geeignet für

Geeigneter Einsatz: Ideal for AI agents that need rag-first codebase search.

Handlungsfähige Anwendungsfälle for rag-first

Anwendungsfall: RAG-First Codebase Search
Anwendungsfall: Call mcp devrag search with your query
Anwendungsfall: Evaluate the results:

! Sicherheit & Einschränkungen

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

About The Source

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

Labs-Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

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

Boot Container Sandbox

FAQ und Installationsschritte

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

? Häufige Fragen

Was ist rag-first?

Geeigneter Einsatz: Ideal for AI agents that need rag-first codebase search. Lokalisierte Zusammenfassung: # 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.

Wie installiere ich rag-first?

Führen Sie den Befehl aus: npx killer-skills add tomharris/dev-rag. Er funktioniert mit Cursor, Windsurf, VS Code, Claude Code und mehr als 19 weiteren IDEs.

Wofür kann ich rag-first verwenden?

Wichtige Einsatzbereiche sind: Anwendungsfall: RAG-First Codebase Search, Anwendungsfall: Call mcp devrag search with your query, Anwendungsfall: Evaluate the results:.

Welche IDEs sind mit rag-first kompatibel?

Dieser Skill ist mit 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 kompatibel. Nutzen Sie die Killer-Skills CLI für eine einheitliche Installation.

Gibt es Einschränkungen bei rag-first?

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

So installieren Sie den Skill

  1. 1. Terminal öffnen

    Öffnen Sie Ihr Terminal oder die Kommandozeile im Projektverzeichnis.

  2. 2. Installationsbefehl ausführen

    Führen Sie aus: npx killer-skills add tomharris/dev-rag. Die CLI erkennt Ihre IDE oder Ihren Agenten automatisch und richtet den Skill ein.

  3. 3. Skill verwenden

    Der Skill ist jetzt aktiv. Ihr KI-Agent kann rag-first sofort im aktuellen Projekt verwenden.

! 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 is adapted 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 is adapted 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.

Verwandte Fähigkeiten

Looking for an alternative to rag-first or another community skill for your workflow? Explore these related open-source skills.

Alle anzeigen

openclaw-release-maintainer

Logo of openclaw
openclaw

Lokalisierte Zusammenfassung: 🦞 # OpenClaw Release Maintainer Use this skill for release and publish-time workflow. It covers ai, assistant, crustacean workflows. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

333.8k
0
Künstliche Intelligenz

widget-generator

Logo of f
f

Lokalisierte Zusammenfassung: Generate customizable widget plugins for the prompts.chat feed system # Widget Generator Skill This skill guides creation of widget plugins for prompts.chat. It covers ai, artificial-intelligence, awesome-list workflows. This AI agent skill supports Claude Code

149.6k
0
Künstliche Intelligenz

flags

Logo of vercel
vercel

Lokalisierte Zusammenfassung: The React Framework # Feature Flags Use this skill when adding or changing framework feature flags in Next.js internals. It covers blog, browser, compiler workflows. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

138.4k
0
Browser

pr-review

Logo of pytorch
pytorch

Lokalisierte Zusammenfassung: Usage Modes No Argument If the user invokes /pr-review with no arguments, do not perform a review. It covers autograd, deep-learning, gpu workflows. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

98.6k
0
Entwickler