<div align="center">

Auto Wiki

Industrial-Grade Code Wiki Auto-Generation Engine · 工业级代码知识库自动生成引擎

English | 简体中文

</div>

A reusable MCP Server + Claude Code Skill that automatically generates 50+ industrial-grade wiki pages from any codebase. Point it at your project, run /autowiki, and it produces deep documentation with Mermaid diagrams, line-level source code citations, and troubleshooting guides.

Recommended: Claude Code with Opus 4.7 for best results — but works with any Claude Code setup.

---

Overview

Auto Wiki scans your project, analyzes dependencies, and generates comprehensive documentation across 10 categories. Each page follows an 11-section template with 7 mandatory quality checks.

Documentation Categories

Category	Coverage
Globals & Standards	Tech stack, directory structure, system overview, developer guide
Frontend Architecture	Routes, state management, component tree, API integration
Backend Architecture (MVC)	Controller, Service, Mapper layers with call chains
Backend Architecture (Design)	Entity models, DTOs, response formats, layered design
Backend Architecture (DI)	Dependency injection, component call chains, startup mechanism
Database Design	ER diagrams, table schemas, indexes, query patterns
Core Business Logic	Domain models, state machines, auction engine, scheduling
API Documentation	Endpoint reference with request/response schemas
Security Design	JWT authentication, RBAC, data protection
Deployment & Config	Environment config, database setup, topology

---

Quick Start

# 1. Install dependencies
npm install

# 2. Register as global MCP Server
claude mcp add autowiki -- node "$(pwd)/index.js"

# 3. Install the skill
mkdir -p ~/.claude/skills/autowiki
cp SKILL.md ~/.claude/skills/autowiki/SKILL.md

# 4. Restart Claude Code, then in any project terminal:
/autowiki

The system will scan your source tree, schedule 50+ documentation tasks in 5 batches, and write everything to docs/wiki/.

---

How It Works

Auto Wiki runs a multi-phase pipeline:

1. Domain-Driven Planning

Scans the project tree and schedules 50+ tasks across 10 categories in 5 batches — globals, database, backend, frontend, core business + API, security + deployment.

2. Dependency Analysis

For each task, analyzes import/require statements to identify related source files, ensuring every document has comprehensive context (≥6 files per page).

3. Serial Writing Loop

One task at a time, the system:

• Reads all related source files with real line numbers
• Writes an 11-section markdown with 4+ Mermaid diagrams per page
• Includes <cite> blocks tracing every claim back to source code

4. Quality Validation

Every submission passes 7 automated checks before saving — word count, section coverage, diagram count, citation count, appendix code examples, Mermaid syntax, and cite block presence.

5. Human Edit Protection

Manually edited blocks (marked with <!-- human-edited -->) are preserved across regenerations — never overwrite your customizations.

---

Use Cases

Scenario	Description
Onboarding	New team members get complete codebase documentation in minutes, not weeks
Legacy Revival	Breathe life into undocumented or abandoned codebases
API Docs	Auto-generated endpoint references with curl examples
Knowledge Preservation	Prevent single-point-of-failure when key developers leave

Not Intended For

• Replacing hand-written design documents that require product-level decisions
• Public-facing developer portals (output is team-internal wiki quality)
• Live preview or real-time editing — it's a batch generation tool

---

Prerequisites

• Node.js ≥ 18
• Claude Code CLI installed and authenticated

Tech Stack

Component	Technology
Runtime	Node.js 18+
Protocol	MCP SDK
File Scanning	fast-glob
Skill Definition	Claude Code SKILL.md

---

MCP Tools

Tool	Function
autowiki_get_project_tree	Scan project source structure
autowiki_analyze_dependencies	AST-based import/require inference
autowiki_scan_files	Glob-pattern file scanning
autowiki_clear_task_queue	Reset task queue
autowiki_add_tasks_to_queue	Batch-add documentation tasks
autowiki_get_pending_task	Fetch next task (injects 11-section template)
autowiki_submit_task	Submit with 7 quality checks
autowiki_append_knowledge	Append fragment knowledge to existing cards

---

Quality Gates

Every submitted document must pass these checks:

Check	Threshold
Word count	≥ 2,000 characters
Citation block	<cite> tag must exist
Cited files	≥ 4 unique source files
Mermaid diagrams	≥ 4 per page (graph, sequence, flowchart, ER/class)
Section coverage	≥ 8 of 11 required sections
Appendix	Runnable code examples (curl, JS, or Java)
Mermaid syntax	No unquoted @ symbols, no ; in ER diagram attributes

On failure, the server returns a detailed list of what's missing — fix and resubmit.

---

Project Structure

autowiki-mcp/
├── index.js                # MCP Server (task queue + validation engine)
├── package.json            # Dependencies
├── SKILL.md                # Claude Code Skill definition (writing rules + pipeline)
├── README.md               # This file
├── .gitignore
├── .github/workflows/
│   └── ci.yml              # GitHub Actions CI
├── test/
│   └── human-edited.test.js
└── docs/wiki/              # Generated wiki output (created at runtime)

---

Commands

npm install     # Install dependencies
npm test        # Run test suite (4 tests)
node index.js   # Start MCP Server (for debugging/manual use)

---

Design Principles

Pure LLM-driven documentation suffers from three defects: output truncation, missing context, and inconsistent structure. Auto Wiki addresses each with engineering:

Defect	Solution
Long JSON output truncation	5-batch scheduling, 8-15 tasks per batch
Missing related source files	autowiki_analyze_dependencies code-level inference
Omitted documentation sections	requiredSections template injection + server-side validation
Not enough diagrams	Enforced minDiagrams: 4, rejection if unmet
Hallucinated line numbers	Forces real line numbers from Read tool output

---

Customization

The SKILL.md file is the single source of truth for document structure, categories, and quality thresholds. Edit it to:

• Add or remove documentation categories
• Adjust quality check thresholds
• Change the 11-section writing template
• Add project-specific terminology rules

For project-specific rules, add a CLAUDE.md or append to your existing one.

---

License

MIT

---

概述

Auto Wiki 是一个 MCP Server + Claude Code Skill，能够从任意代码库自动生成 50+ 篇工业级 Wiki 文档。在目标项目中运行 /autowiki，即可获得带 Mermaid 图表、行级代码溯源、故障排查指南的深度文档。

推荐搭配： Claude Code + Opus 4.7 效果最佳，其他版本亦可使用。

文档分类

分类	覆盖范围
全局与规范	技术栈、目录结构、系统概述、开发者指南
前端架构	路由、状态管理、组件树、API 集成
后端架构 (MVC)	Controller、Service、Mapper 层及调用链路
后端架构 (分层设计)	实体模型、DTO、统一响应、分层规范
后端架构 (依赖管理)	DI 机制、组件调用链、启动流程
数据库设计	ER 图、表结构、索引、典型查询
核心功能设计	领域模型、状态机、业务引擎、定时任务
API 接口文档	端点参考、请求/响应结构
安全设计	JWT 认证、RBAC 权限、数据保护
部署与配置	环境配置、数据库配置、部署拓扑

快速开始

# 1. 安装依赖
npm install

# 2. 注册为全局 MCP Server
claude mcp add autowiki -- node "$(pwd)/index.js"

# 3. 安装技能
mkdir -p ~/.claude/skills/autowiki
cp SKILL.md ~/.claude/skills/autowiki/SKILL.md

# 4. 重启 Claude Code，在任意项目中输入：
/autowiki

系统会自动扫描源码树、分 5 批排期 50+ 个文档任务，全部输出到 docs/wiki/ 目录。

工作流程

1. 领域排期 — 按 10 大分类分 5 批追加任务
2. 依赖分析 — 通过 import/require 推导关联文件，确保每篇文档 ≥6 个引用文件
3. 串行撰写 — 逐个读取源文件 → 按 11 段模板撰写 → 嵌入 4+ 个 Mermaid 图表
4. 质量校验 — 7 项自动检查，不达标详细打回
5. 人工保护 — <!-- human-edited --> 区块在重新生成时自动保留

适用场景

场景	说明
团队 onboarding	新成员几分钟内获完整代码库文档
遗留系统重建	为无文档或废弃项目生成深度文档
API 文档	自动生成带 curl 示例的接口文档
知识留存	防止核心人员离开导致知识断层

质量校验

检查项	阈值
字数	≥ 2000 字符
溯源区块	必须包含 <cite> 标签
引用文件	≥ 4 个唯一文件
Mermaid 图表	≥ 4 个（graph/sequence/flowchart/ER-class）
章节覆盖	≥ 8/11 段
附录代码	可运行的 curl、JS 或 Java 示例
Mermaid 语法	@ 符号必须加引号，ER 属性不能有分号

项目结构

autowiki-mcp/
├── index.js                # MCP Server 主程序（任务队列 + 校验引擎）
├── package.json            # 依赖声明
├── SKILL.md                # Claude Code Skill 定义（写作规范 + 排期管线）
├── README.md               # 本文件
├── .gitignore
├── .github/workflows/
│   └── ci.yml              # GitHub Actions CI
├── test/
│   └── human-edited.test.js
└── docs/wiki/              # 生成的 Wiki 文档目录（运行时自动创建）

设计原理

纯 LLM 驱动的文档生成有三大缺陷：输出截断、上下文遗漏、结构不一致。本引擎通过以下手段逐一破解：

缺陷	对策
长 JSON 输出截断	分 5 批次排期，每次只追加 8-15 个任务
找不全关联文件	autowiki_analyze_dependencies 代码级依赖推导
文档章节遗漏	requiredSections 模板注入 + 服务端校验打回
图表数量不足	强制 minDiagrams: 4，不达标拒绝保存
行号幻觉	强制引用 Read 工具返回的真实行号

自定义扩展

编辑 SKILL.md 即可调整文档结构、分类树和质量阈值。项目级别的配置可在 CLAUDE.md 中添加。

命令

npm install     # 安装依赖
npm test        # 运行测试（4 个用例）
node index.js   # 启动 MCP Server（调试用）

License

MIT