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codebase-onboarding

分析陌生代码的结构、请求处理流程和开发惯例,生成项目入门指南与CLAUDE.md,并整理不同修改任务应查看的位置,帮助了解项目全貌。

原文查看原文说明

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 评分100分/ 100查看安装方法

适用场景

  • 加入新团队时了解代码全貌
  • 在现有项目中引入Claude Code
  • 根据开发惯例创建CLAUDE.md
  • 更新CLAUDE.md以符合现状

中文介绍

功能

codebase-onboarding分析陌生代码,生成项目入门指南和CLAUDE.md。它通过配置文件和目录识别技术栈、处理入口与测试环境,梳理请求从输入到响应的流程。同时检查命名、错误处理、测试及Git工作惯例,汇总开发、构建等命令,以及不同任务应查看的位置。

适用场景

适合刚加入新团队,或首次在现有项目中使用Claude Code的情况。希望在阅读全部文件前掌握项目全貌,或整理需要传达给智能体的开发规则时,也可使用。

使用示例

  • “介绍这个代码库的整体结构,以及新增功能时应查看的位置。”
  • “按照这个项目的惯例创建CLAUDE.md。”
  • “将当前开发规则更新到现有的CLAUDE.md中。”

注意事项

已有CLAUDE.md时,会保留项目特有的指令,并明确列出新增和修改内容。无法判断的惯例会标为未知,不作猜测。没有Git历史或历史较浅时,会跳过根据历史判断工作惯例的部分。资料中的技术和命令示例不会直接照搬,而是替换为从目标项目中检测到的内容。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好明确说明在加入项目或引入Claude Code时分析代码,生成入门指南和CLAUDE.md。
步骤具体性步骤、输入输出和示例是否具体
A良好提供四阶段调查步骤、指南与CLAUDE.md的输出模板,以及请求示例。
安全性发送数据、删除文件、执行命令等是否有说明
A良好说明CLAUDE.md的写入范围,并要求保留现有指令、明确列出变更。
安装难易度所需工具和 API 密钥是否少且有说明
A良好未要求专用API密钥或额外服务,并说明Git历史缺失或较浅时的处理方式。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(1)

  • SKILL.md8.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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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