codebase-onboarding
alirezarezvani/claude-skills
分析代码库,并为工程师、技术负责人和外包人员生成入职文档,能够快速收集信息并生成可重复使用的输出结果。
...展开全部Codebase 入职指南
级别:POWERFUL
类别:工程
领域:文档 / 开发者体验
概述
分析代码库,并为工程师、技术负责人和外包人员生成入职文档。该技能专为快速收集事实信息和生成可重复的入职文档而优化。
核心能力
- 基于代码库信号发现架构与技术栈
- 为新贡献者整理关键文件和配置清单
- 本地环境配置及常见任务指南生成
- 针对受众定制的文档框架
- 调试与贡献检查清单的框架生成
适用场景
- 接纳新团队成员或外包人员时
- 在大规模重构后重建过时的项目文档
- 准备内部交接文档
- 为服务创建标准化的入职资料包
快速入门
# 1) 收集代码库信息
python3 scripts/codebase_analyzer.py /path/to/repo
# 2) 导出机器可读的输出结果
python3 scripts/codebase_analyzer.py /path/to/repo --json
# 3) 使用模板起草入职文档
# 参见 references/onboarding-template.md
推荐工作流
- 对目标代码库运行
scripts/codebase_analyzer.py。 - 提取关键指标:文件数量、检测到的编程语言、配置文件、顶级结构。
- 填写
references/onboarding-template.md中的入职模板。 - 根据受众调整输出深度:
- 初级:环境搭建 + 安全防护措施
- 资深:架构 + 运维考量
- 外包人员:明确职责范围 + 集成边界
入职文档模板
详细的模板和章节示例位于:
references/onboarding-template.mdreferences/output-format-templates.md
常见陷阱
- 在未对干净环境中的配置命令进行验证的情况下编写文档
- 将架构深度解析内容混入面向承包商的文档中
- 遗漏故障排除和验证步骤
- 让入门文档与当前代码库状态脱节
最佳实践
- 确保环境配置说明可执行且有时间限制。
- 记录关键架构决策的“原因”。
- 在行为变更的同一拉取请求中更新文档。
- 将入职文档视为动态的运营资产,而非一次性交付物。
---
name: codebase-onboarding
description: Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors, with fast fact-gathering and repeatable outputs.
---
# 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.





首页
