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代码审查工具

基于 JavaScript · 开源免费，本地部署，数据完全自主可控

英文名：brooks-lint

⭐ 186 Stars 🍴 12 Forks 💻 JavaScript 📄 MIT 🏷 AI 8.5分

8.5AI 综合评分

ai-code-reviewarchitecture-reviewauto-fix

🌐 访问官网

✦ AI Skill Hub 推荐

代码审查工具是 AI Skill Hub 本期精选AI工具之一。综合评分 8.5 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。

📚 深度解析

代码审查工具是一款基于 JavaScript 的开源工具，在 GitHub 上收获 0k+ Star，是ai-code-review、architecture-review、auto-fix领域中的优质开源项目。开源工具的最大优势在于代码完全透明，你可以审计每一行代码的安全性，也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。

**安装与环境准备**
代码审查工具依赖 JavaScript 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 JavaScript 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪代码审查工具的版本更新，及时通知重要功能变化。

📋 工具概览

基于12本经典工程书籍的AI代码审查，诊断代码腐烂风险

代码审查工具是一款基于 JavaScript 开发的开源工具，专注于 ai-code-review、architecture-review、auto-fix 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。

GitHub Stars

⭐ 186

开发语言

JavaScript

支持平台

Windows / macOS / Linux

维护状态

轻量级项目，按需更新

开源协议

MIT

AI 综合评分

8.5 分

工具类型

AI工具

Forks

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

基于12本经典工程书籍的AI代码审查，诊断代码腐烂风险

📌 核心特色

开源免费，支持本地部署，数据完全自主可控
活跃的 GitHub 开源社区，持续迭代更新
提供详细文档和使用示例，新手友好
支持自定义配置，灵活适配不同使用环境
可作为基础组件集成进现有技术栈或进行二次开发

🎯 主要使用场景

本地部署运行，保护数据隐私，满足合规要求
自定义集成到现有系统，扩展技术栈能力
作为开源基础组件进行商业化二次开发

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 方式一：npm 全局安装
npm install -g brooks-lint

# 方式二：npx 直接运行（无需安装）
npx brooks-lint --help

# 方式三：项目依赖安装
npm install brooks-lint

# 方式四：从源码运行
git clone https://github.com/hyhmrright/brooks-lint
cd brooks-lint
npm install
npm start

📋 安装步骤说明

访问 GitHub 仓库页面
按照 README 文档完成依赖安装
根据系统环境完成初始化配置
参考官方示例或文档开始使用
遇到问题可在 GitHub Issues 中查找解答

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 命令行使用
brooks-lint --help

# 基本用法
brooks-lint [options] <input>

# Node.js 代码中使用
const brooks_lint = require('brooks-lint');

const result = await brooks_lint.run(options);
console.log(result);

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

# brooks-lint 配置说明
# 查看配置选项
brooks-lint --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量（覆盖配置文件）
export BROOKS_LINT_CONFIG="/path/to/config.yml"

📑 README 深度解析真实文档完整度 63/100 含工作流图查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

简介

brooks-lint

AI code reviews grounded in twelve classic engineering books. Consistent. Traceable. Actionable.

English · <a href="README.zh-CN.md">简体中文</a>

<a href="#the-six-decay-risks">The Six Decay Risks</a> • <a href="#what-it-looks-like">What It Looks Like</a> • <a href="#benchmark">Benchmark</a> • <a href="#installation">Installation</a>

<a href="https://hyhmrright.github.io/brooks-lint/">→ Visit the website</a>

---

"The bearing of a child takes nine months, no matter how many women are assigned." — Frederick Brooks, The Mythical Man-Month (1975)

50 years later, Brooks was still right — and so were McConnell, Fowler, Martin, Hunt & Thomas, Evans, Ousterhout, Winters, Meszaros, Osherove, Feathers, and the Google Testing team.

Most code quality tools count lines and cyclomatic complexity. brooks-lint goes deeper — it diagnoses your code against six decay risk dimensions synthesized from twelve classic engineering books, producing structured findings with book citations, severity labels, and concrete remedies every time.

For the full source-to-skill mapping, including exceptions and false-positive guards, see skills/_shared/source-coverage.md.

Architecture Audit with Dependency Graph

In Mode 2 (Architecture Audit), brooks-lint generates a Mermaid dependency graph at the top of the report. Modules are color-coded by severity: red = Critical findings, yellow = Warning, green = clean.

graph TD subgraph src/api AuthController UserController end subgraph src/domain UserService OrderService end subgraph src/infra Database EmailClient end AuthController --> UserService UserController --> UserService UserController --> OrderService OrderService --> UserService OrderService --> EmailClient UserService --> Database EmailClient -.->|circular| OrderService classDef critical fill:#ff6b6b,stroke:#c92a2a,color:#fff classDef warning fill:#ffd43b,stroke:#e67700 classDef clean fill:#51cf66,stroke:#2b8a3e,color:#fff class OrderService,EmailClient critical class AuthController warning class UserService,UserController,Database clean

The graph renders natively in GitHub, Notion, and other Markdown environments — no extra tools needed.

Installation

See More Examples

The Full Gallery has real brooks-lint output across Python, TypeScript, Go, and Java — including PR reviews, architecture audits with Mermaid dependency graphs, tech debt assessments, and test quality reviews.

New to the decay risks? The Decay Risk Field Guide explains all six — diagnostic question, signature symptoms, source books, and remedy for each.

---

Usage

Configuration

Place a .brooks-lint.yaml in your project root to customize review behavior:

version: 1

disable:
  - T5   # skip coverage metrics check — we don't enforce coverage

severity:
  R1: suggestion   # downgrade Cognitive Overload findings for this domain

ignore:
  - "**/*.generated.*"
  - "**/vendor/**"

Copy .brooks-lint.example.yaml as a starting point. All settings are optional — omit the file entirely for default behavior.

Setting	Description
`disable`	Risk codes to skip (`R1`–`R6`, `T1`–`T6`)
`severity`	Override severity tier (`critical` / `warning` / `suggestion`)
`ignore`	Glob patterns for files to exclude
`focus`	Evaluate only these risk codes (cannot combine with `disable`)

---

CI/CD Integration

Automate brooks-lint on every PR using the GitHub Action:

```yaml

.github/workflows/brooks-lint.yml

name: Brooks-Lint PR Review on: pull_request: types: [opened, synchronize, reopened]

jobs: brooks-lint: runs-on: ubuntu-latest permissions: pull-requests: write steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: hyhmrright/brooks-lint/.github/actions/brooks-lint@main with: mode: review anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }} fail-below: 70 ```

See docs/github-action-example.yml for the full template.

The action posts the review as a PR comment and optionally fails the check if the Health Score drops below a threshold. If .brooks-lint-history.json is committed to your repo, the comment also includes a trend delta (e.g., "85 → 82 (−3) over last 3 runs").

Cost: ~$0.05–0.15 per PR run depending on diff size and model. Recommend running on pull_request events only.

How It Compares

	brooks-lint	ESLint / Pylint	GitHub Copilot Review	Plain Claude
Detects syntax & style issues	—	✅	✅	~
Structured diagnosis chain	✅	❌	❌	❌
Traces findings to classic books	✅	❌	❌	❌
Consistent severity labels	✅	✅	~	❌
Architecture-level insights	✅	❌	~	~
Domain model analysis	✅	❌	❌	~
Zero config, no plugins to install	✅	❌	✅	✅
Works with any language	✅	❌	✅	✅

~ = occasionally / inconsistently

brooks-lint doesn't replace your linter. It catches what linters can't: architectural drift, knowledge silos, and domain model distortion — the problems that slow teams down for months before anyone notices.

🎯 aiskill88 AI 点评 A 级 2026-06-01

高质量的AI代码审查工具，基于经典工程书籍