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codebase-onboarding

Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. Fast fact-gathering and repeatable onboarding outputs. Use when onboarding a new engineer, writing architecture-overview docs for a new project, or producing tech-lead briefings for unfamiliar repos.

0 次下载 0 次浏览 更新于 Oct 31, 2026 v1.0.0
codebase-onboarding skill cover

下方技能配置(提示词、变量、安装步骤)保留英文原文。.aiskill 文件直接面向 AI 智能体,统一使用英文以确保跨平台行为一致。

概述

Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. Fast fact-gathering and repeatable onboarding outputs. Use when onboarding a new engineer, writing architecture-overview docs for a new project, or producing tech-lead briefings for unfamiliar repos.

Source: https://github.com/alirezarezvani/claude-skills · MIT · © 2025 Alireza Rezvani

系统提示词

定义智能体角色、规则与输出格式的指令块。

system-prompt.txt
# Codebase Onboarding

**Tier:** POWERFUL  
**Category:** Engineering  
**Domain:** Documentation / Developer Experience

---

## Overview

Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. This skill is optimized for fast fact-gathering and repeatable onboarding outputs.

## Core Capabilities

- Architecture and stack discovery from repository signals
- Key file and config inventory for new contributors
- Local setup and common-task guidance generation
- Audience-aware documentation framing
- Debugging and contribution checklist scaffolding

---

## When to Use

- Onboarding a new team member or contractor
- Rebuilding stale project docs after large refactors
- Preparing internal handoff documentation
- Creating a standardized onboarding packet for services

---

## Quick Start

```bash
# 1) Gather codebase facts
python3 scripts/codebase_analyzer.py /path/to/repo

# 2) Export machine-readable output
python3 scripts/codebase_analyzer.py /path/to/repo --json

# 3) Use the template to draft onboarding docs
# See references/onboarding-template.md
```

---

## Recommended Workflow

1. Run `scripts/codebase_analyzer.py` against the target repository.
2. Capture key signals: file counts, detected languages, config files, top-level structure.
3. Fill the onboarding template in `references/onboarding-template.md`.
4. Tailor output depth by audience:
   - Junior: setup + guardrails
   - Senior: architecture + operational concerns
   - Contractor: scoped ownership + integration boundaries

---

## Onboarding Document Template

Detailed template and section examples live in:
- `references/onboarding-template.md`
- `references/output-format-templates.md`

---

## Common Pitfalls

- Writing docs without validating setup commands on a clean environment
- Mixing architecture deep-dives into contractor-oriented docs
- Omitting troubleshooting and verification steps
- Letting onboarding docs drift from current repo state

## Best Practices

1. Keep setup instructions executable and time-bounded.
2. Document the "why" for key architectural decisions.
3. Update docs in the same PR as behavior changes.
4. Treat onboarding docs as living operational assets, not one-time deliverables.

用户提示词模板

填充变量后发送,即可运行该技能: {{variables}}

user-template.txt

变量

名称 必填 说明 示例

完整技能配置

标准 .aiskill 文件,提供 JSON 与 YAML 两种格式。

codebase-onboarding.aiskill (JSON)
{
  "format": "aiskill/v1",
  "skill": {
    "name": "codebase-onboarding",
    "slug": "codebase-onboarding",
    "version": "1.0.0",
    "description": "Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. Fast fact-gathering and repeatable onboarding outputs. Use when onboarding a new engineer, writing architecture-overview docs for a new project, or producing tech-lead briefings for unfamiliar repos.",
    "category": "Development",
    "tags": [
      "dev-workflow",
      "content"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "# Codebase Onboarding\r\n\r\n**Tier:** POWERFUL  \r\n**Category:** Engineering  \r\n**Domain:** Documentation / Developer Experience\r\n\r\n---\r\n\r\n## Overview\r\n\r\nAnalyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. This skill is optimized for fast fact-gathering and repeatable onboarding outputs.\r\n\r\n## Core Capabilities\r\n\r\n- Architecture and stack discovery from repository signals\r\n- Key file and config inventory for new contributors\r\n- Local setup and common-task guidance generation\r\n- Audience-aware documentation framing\r\n- Debugging and contribution checklist scaffolding\r\n\r\n---\r\n\r\n## When to Use\r\n\r\n- Onboarding a new team member or contractor\r\n- Rebuilding stale project docs after large refactors\r\n- Preparing internal handoff documentation\r\n- Creating a standardized onboarding packet for services\r\n\r\n---\r\n\r\n## Quick Start\r\n\r\n```bash\r\n# 1) Gather codebase facts\r\npython3 scripts/codebase_analyzer.py /path/to/repo\r\n\r\n# 2) Export machine-readable output\r\npython3 scripts/codebase_analyzer.py /path/to/repo --json\r\n\r\n# 3) Use the template to draft onboarding docs\r\n# See references/onboarding-template.md\r\n```\r\n\r\n---\r\n\r\n## Recommended Workflow\r\n\r\n1. Run `scripts/codebase_analyzer.py` against the target repository.\r\n2. Capture key signals: file counts, detected languages, config files, top-level structure.\r\n3. Fill the onboarding template in `references/onboarding-template.md`.\r\n4. Tailor output depth by audience:\r\n   - Junior: setup + guardrails\r\n   - Senior: architecture + operational concerns\r\n   - Contractor: scoped ownership + integration boundaries\r\n\r\n---\r\n\r\n## Onboarding Document Template\r\n\r\nDetailed template and section examples live in:\r\n- `references/onboarding-template.md`\r\n- `references/output-format-templates.md`\r\n\r\n---\r\n\r\n## Common Pitfalls\r\n\r\n- Writing docs without validating setup commands on a clean environment\r\n- Mixing architecture deep-dives into contractor-oriented docs\r\n- Omitting troubleshooting and verification steps\r\n- Letting onboarding docs drift from current repo state\r\n\r\n## Best Practices\r\n\r\n1. Keep setup instructions executable and time-bounded.\r\n2. Document the \"why\" for key architectural decisions.\r\n3. Update docs in the same PR as behavior changes.\r\n4. Treat onboarding docs as living operational assets, not one-time deliverables.",
    "user_template": ""
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The codebase-onboarding/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/codebase-onboarding",
    "exported_at": "2026-11-03T07:40:59.991Z"
  }
}
codebase-onboarding.aiskill (YAML)
format: aiskill/v1
skill:
  name: codebase-onboarding
  slug: codebase-onboarding
  version: 1.0.0
  description: >-
    Analyze a codebase and generate onboarding documentation for engineers, tech leads, and
    contractors. Fast fact-gathering and repeatable onboarding outputs. Use when onboarding a new
    engineer, writing architecture-overview docs for a new project, or producing tech-lead briefings
    for unfamiliar repos.
  category: Development
  tags:
    - dev-workflow
    - content
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# Codebase Onboarding\r\n\r\n**Tier:** POWERFUL  \r\n**Category:** Engineering  \r\n**Domain:** Documentation / Developer Experience\r\n\r\n---\r\n\r\n## Overview\r\n\r\nAnalyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. This skill is optimized for fast fact-gathering and repeatable onboarding outputs.\r\n\r\n## Core Capabilities\r\n\r\n- Architecture and stack discovery from repository signals\r\n- Key file and config inventory for new contributors\r\n- Local setup and common-task guidance generation\r\n- Audience-aware documentation framing\r\n- Debugging and contribution checklist scaffolding\r\n\r\n---\r\n\r\n## When to Use\r\n\r\n- Onboarding a new team member or contractor\r\n- Rebuilding stale project docs after large refactors\r\n- Preparing internal handoff documentation\r\n- Creating a standardized onboarding packet for services\r\n\r\n---\r\n\r\n## Quick Start\r\n\r\n```bash\r\n# 1) Gather codebase facts\r\npython3 scripts/codebase_analyzer.py /path/to/repo\r\n\r\n# 2) Export machine-readable output\r\npython3 scripts/codebase_analyzer.py /path/to/repo --json\r\n\r\n# 3) Use the template to draft onboarding docs\r\n# See references/onboarding-template.md\r\n```\r\n\r\n---\r\n\r\n## Recommended Workflow\r\n\r\n1. Run `scripts/codebase_analyzer.py` against the target repository.\r\n2. Capture key signals: file counts, detected languages, config files, top-level structure.\r\n3. Fill the onboarding template in `references/onboarding-template.md`.\r\n4. Tailor output depth by audience:\r\n   - Junior: setup + guardrails\r\n   - Senior: architecture + operational concerns\r\n   - Contractor: scoped ownership + integration boundaries\r\n\r\n---\r\n\r\n## Onboarding Document Template\r\n\r\nDetailed template and section examples live in:\r\n- `references/onboarding-template.md`\r\n- `references/output-format-templates.md`\r\n\r\n---\r\n\r\n## Common Pitfalls\r\n\r\n- Writing docs without validating setup commands on a clean environment\r\n- Mixing architecture deep-dives into contractor-oriented docs\r\n- Omitting troubleshooting and verification steps\r\n- Letting onboarding docs drift from current repo state\r\n\r\n## Best Practices\r\n\r\n1. Keep setup instructions executable and time-bounded.\r\n2. Document the \"why\" for key architectural decisions.\r\n3. Update docs in the same PR as behavior changes.\r\n4. Treat onboarding docs as living operational assets, not one-time deliverables."
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The codebase-onboarding/ folder
        contains SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/codebase-onboarding
  exported_at: "2026-11-03T07:40:59.991Z"

安装指南

  1. 1

    Download & unzip

    Download the .zip package from SkillVault and unzip it. The codebase-onboarding/ folder contains SKILL.md plus any companion scripts, references and assets.

  2. 2

    Copy into your agent skills directory

    Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session.

  3. 3

    Use it

    Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description.

使用方法

平台兼容性

ChatGPT GPTs

OpenAI

将系统提示词粘贴到 GPT 的「Instructions」字段,并把用户模板作为起始消息使用。

官方文档

Claude Code

Anthropic

将导出的 .aiskill JSON 保存为项目中的技能文件,或将系统提示词粘贴进 CLAUDE.md / 斜杠命令 Markdown 文件。

官方文档

Cursor

Anysphere

将系统提示词复制到 .cursor/rules/*.mdc(或设置中的 Project Rules),并在智能体提示词中引用用户模板。

官方文档

相关技能