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codebase-onboarding

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by Api.AirforcePrepends a system promptAI & Agent Building000 uses202,700

Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.

open-sourceclaude-codeai-agent-buildingaffaan-m
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What this skill does

When applied, it prepends a system prompt before your request is sent — no extra calls and no change to how you are billed beyond the added tokens.

---
name: codebase-onboarding
description: Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.
origin: ECC
---

# Codebase Onboarding

Systematically analyze an unfamiliar codebase and produce a structured onboarding guide. Designed for developers joining a new project or setting up Claude Code in an existing repo for the first time.

## When to Use

- First time opening a project with Claude Code
- Joining a new team or repository
- User asks "help me understand this codebase"
- User asks to generate a CLAUDE.md for a project
- User says "onboard me" or "walk me through this repo"

## How It Works

### Phase 1: Reconnaissance

Gather raw signals about the project without reading every file. Run these checks in parallel:

```
1. Package manifest detection
   → package.json, go.mod, Cargo.toml, pyproject.toml, pom.xml, build.gradle,
     Gemfile, composer.json, mix.exs, pubspec.yaml

2. Framework fingerprinting
   → next.config.*, nuxt.config.*, angular.json, vite.config.*,
     django settings, flask app factory, fastapi main, rails config

3. Entry point identification
   → main.*, index.*, app.*, server.*, cmd/, src/main/

4. Directory structure snapshot
   → Top 2 levels of the directory tree, ignoring node_modules, vendor,
     .git, dist, build, __pycache__, .next

5. Config and tooling detection
   → .eslintrc*, .prettierrc*, tsconfig.json, Makefile, Dockerfile,
     docker-compose*, .github/workflows/, .env.example, CI configs

6. Test structure detection
   → tests/, test/, __tests__/, *_test.go, *.spec.ts, *.test.js,
     pytest.ini, jest.config.*, vitest.config.*
```

### Phase 2: Architecture Mapping

From the reconnaissance data, identify:

**Tech Stack**
- Language(s) and version constraints
- Framework(s) and major libraries
- Database(s) and ORMs
- Build tools and bu

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Per request

Add a "skill" field with the skill’s ID to your chat completion request. It is applied server-side before your prompt is sent — no extra calls.

{
  "model": "gpt-4o-mini",
  "skill": "imp-d4ad79dc-cfa5-4167-9a2a-573298458c7e",
  "messages": [{ "role": "user", "content": "…" }]
}
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