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codebase-onboarding

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.

O que é codebase-onboarding?

codebase-onboarding is a Claude Code agent skill that 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.

Funciona comClaude Code~Codex CLI~Cursor
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Documentação

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:

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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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