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codebase-onboarding

Explore an unfamiliar codebase and turn its structure, request flow, and development conventions into a concise onboarding guide. Create or update CLAUDE.md with project-specific instructions, preserving existing guidance and identifying findings that remain uncertain.

OriginalShow the original 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.

AI rating100/ 100See how to install

Good for

  • Understanding an unfamiliar project when joining a team
  • Setting up Claude Code in an existing repository
  • Creating CLAUDE.md from detected project conventions
  • Updating existing CLAUDE.md with current practices

Overview

What it does

codebase-onboarding examines an unfamiliar codebase and creates an onboarding guide and CLAUDE.md. It uses configuration files and directories to identify the technology stack, entry points, and test setup, then traces a request from input to response. It also reviews naming, error handling, testing, and Git conventions. The resulting guidance includes development and build commands, plus places to look for different kinds of changes.

When it helps

Use it when joining a new team or introducing Claude Code to an existing project. It helps you understand the overall structure before reading every file and organize the development rules you want an agent to follow.

Example requests

  • “Explain this codebase’s overall structure and where to look when adding a feature.”
  • “Create a CLAUDE.md that follows this project’s conventions.”
  • “Update the existing CLAUDE.md with the current development rules.”

Notes

If CLAUDE.md already exists, updates preserve its project-specific instructions and identify additions and changes. Conventions that cannot be determined are reported as unknown rather than guessed. When Git history is unavailable or shallow, history-based convention detection is skipped. Technologies and command examples in the reference material are replaced with findings from the actual project.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt clearly targets project onboarding and Claude Code setup through codebase analysis, an onboarding guide, and CLAUDE.md.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodFour analysis phases, output templates for the guide and CLAUDE.md, and example requests are provided.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodThe CLAUDE.md write scope is defined, with explicit requirements to preserve existing instructions and identify changes.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodNo dedicated API keys or additional services are required, and missing or shallow Git history has an explicit fallback.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

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SKILL.md (original)

Review the instructions the agent will receive before you install.

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 bundlers
  • CI/CD platform

Architecture Pattern

  • Monolith, monorepo, microservices, or serverless
  • Frontend/backend split or full-stack
  • API style: REST, GraphQL, gRPC, tRPC

Key Directories Map the top-level directories to their purpose:

<!-- Example for a React project — replace with detected directories -->
src/components/  → React UI components
src/api/         → API route handlers
src/lib/         → Shared utilities
src/db/          → Database models and migrations
tests/           → Test suites
scripts/         → Build and deployment scripts

Data Flow Trace one request from entry to response:

  • Where does a request enter? (router, handler, controller)
  • How is it validated? (middleware, schemas, guards)
  • Where is business logic? (services, models, use cases)
  • How does it reach the database? (ORM, raw queries, repositories)

Phase 3: Convention Detection

Identify patterns the codebase already follows:

Naming Conventions

  • File naming: kebab-case, camelCase, PascalCase, snake_case
  • Component/class naming patterns
  • Test file naming: *.test.ts, *.spec.ts, *_test.go

Code Patterns

  • Error handling style: try/catch, Result types, error codes
  • Dependency injection or direct imports
  • State management approach
  • Async patterns: callbacks, promises, async/await, channels

Git Conventions

  • Branch naming from recent branches
  • Commit message style from recent commits
  • PR workflow (squash, merge, rebase)
  • If the repo has no commits yet or only a shallow history (e.g. git clone --depth 1), skip this section and note "Git history unavailable or too shallow to detect conventions"

Phase 4: Generate Onboarding Artifacts

Produce two outputs:

Output 1: Onboarding Guide

# Onboarding Guide: [Project Name]

## Overview
[2-3 sentences: what this project does and who it serves]

## Tech Stack
<!-- Example for a Next.js project — replace with detected stack -->
| Layer | Technology | Version |
|-------|-----------|---------|
| Language | TypeScript | 5.x |
| Framework | Next.js | 14.x |
| Database | PostgreSQL | 16 |
| ORM | Prisma | 5.x |
| Testing | Jest + Playwright | - |

## Architecture
[Diagram or description of how components connect]

## Key Entry Points
<!-- Example for a Next.js project — replace with detected paths -->
- **API routes**: `src/app/api/` — Next.js route handlers
- **UI pages**: `src/app/(dashboard)/` — authenticated pages
- **Database**: `prisma/schema.prisma` — data model source of truth
- **Config**: `next.config.ts` — build and runtime config

## Directory Map
[Top-level directory → purpose mapping]

## Request Lifecycle
[Trace one API request from entry to response]

## Conventions
- [File naming pattern]
- [Error handling approach]
- [Testing patterns]
- [Git workflow]

## Common Tasks
<!-- Example for a Node.js project — replace with detected commands -->
- **Run dev server**: `npm run dev`
- **Run tests**: `npm test`
- **Run linter**: `npm run lint`
- **Database migrations**: `npx prisma migrate dev`
- **Build for production**: `npm run build`

## Where to Look
<!-- Example for a Next.js project — replace with detected paths -->
| I want to... | Look at... |
|--------------|-----------|
| Add an API endpoint | `src/app/api/` |
| Add a UI page | `src/app/(dashboard)/` |
| Add a database table | `prisma/schema.prisma` |
| Add a test | `tests/` matching the source path |
| Change build config | `next.config.ts` |

Output 2: Starter CLAUDE.md

Generate or update a project-specific CLAUDE.md based on detected conventions. If CLAUDE.md already exists, read it first and enhance it — preserve existing project-specific instructions and clearly call out what was added or changed.

# Project Instructions

## Tech Stack
[Detected stack summary]

## Code Style
- [Detected naming conventions]
- [Detected patterns to follow]

## Testing
- Run tests: `[detected test command]`
- Test pattern: [detected test file convention]
- Coverage: [if configured, the coverage command]

## Build & Run
- Dev: `[detected dev command]`
- Build: `[detected build command]`
- Lint: `[detected lint command]`

## Project Structure
[Key directory → purpose map]

## Conventions
- [Commit style if detectable]
- [PR workflow if detectable]
- [Error handling patterns]

Best Practices

  1. Don't read everything — reconnaissance should use Glob and Grep, not Read on every file. Read selectively only for ambiguous signals.
  2. Verify, don't guess — if a framework is detected from config but the actual code uses something different, trust the code.
  3. Respect existing CLAUDE.md — if one already exists, enhance it rather than replacing it. Call out what's new vs existing.
  4. Stay concise — the onboarding guide should be scannable in 2 minutes. Details belong in the code, not the guide.
  5. Flag unknowns — if a convention can't be confidently detected, say so rather than guessing. "Could not determine test runner" is better than a wrong answer.

Anti-Patterns to Avoid

  • Generating a CLAUDE.md that's longer than 100 lines — keep it focused
  • Listing every dependency — highlight only the ones that shape how you write code
  • Describing obvious directory names — src/ doesn't need an explanation
  • Copying the README — the onboarding guide adds structural insight the README lacks

Examples

Example 1: First time in a new repo

User: "Onboard me to this codebase" Action: Run full 4-phase workflow → produce Onboarding Guide + Starter CLAUDE.md Output: Onboarding Guide printed directly to the conversation, plus a CLAUDE.md written to the project root

Example 2: Generate CLAUDE.md for existing project

User: "Generate a CLAUDE.md for this project" Action: Run Phases 1-3, skip Onboarding Guide, produce only CLAUDE.md Output: Project-specific CLAUDE.md with detected conventions

Example 3: Enhance existing CLAUDE.md

User: "Update the CLAUDE.md with current project conventions" Action: Read existing CLAUDE.md, run Phases 1-3, merge new findings Output: Updated CLAUDE.md with additions clearly marked

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