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Code Review

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smallnest
code-review

专业的代码审查助手，提供结构化的审查流程、详细的检查清单和建设性的反馈指南。适用于审查Pull Request、代码提交或任何需要代码质量评估的场景，涵盖功能性、安全性、性能、测试、文档、架构设计等多个维度。

Overview

Publishersmallnest
Repositorylanggraphgo
Skill namecode-review
Stars
304
Forks
51
Bundled files
2
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by smallnest on GitHub. Read the source before you install it.

Installation

Install the Code Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/smallnest/langgraphgo.git /tmp/langgraphgo
mkdir -p .claude/skills
cp -r /tmp/langgraphgo/testdata/skills/code-review/code-review .claude/skills/code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Code Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Code Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Code Review Skill

Overview

这个Skill提供了专业的代码审查指导，帮助进行全面、高质量的代码审查。当用户请求代码审查、上传代码文件寻求反馈，或询问代码改进建议时，Claude会使用此Skill来提供系统化的审查和建设性的反馈。

审查流程

第一步：理解上下文

在开始审查前，确保理解：

  1. 代码目的：这段代码要实现什么功能？
  2. 变更范围：修改了哪些部分？为什么修改？
  3. 业务背景：相关的需求或用户故事是什么？
  4. 技术栈：使用的语言、框架和工具

第二步：结构化审查

按照以下优先级进行审查：

🔴 高优先级（必须检查）
  1. 功能正确性：代码是否实现了预期功能？
  2. 安全性：是否存在安全漏洞？
  3. 严重Bug：是否有逻辑错误或可能导致崩溃的问题？
🟡 中优先级（强烈建议）
  1. 性能：是否存在性能瓶颈？
  2. 测试覆盖：是否有足够的测试？
  3. 错误处理：异常情况是否妥善处理？
  4. 架构设计：设计是否合理、可扩展？
🟢 低优先级（建议改进）
  1. 代码质量：命名、格式、注释是否清晰？
  2. 文档：是否有必要的文档说明？
  3. 最佳实践：是否遵循行业和项目规范？

第三步：提供反馈

使用建设性的方式提供反馈：

  • 标注问题严重程度（🔴 必须修复、🟡 强烈建议、🟢 建议、💡 可选）
  • 说明问题的影响和原因
  • 提供具体的改进建议和示例代码
  • 认可好的实践

审查输出格式

使用以下结构化格式输出审查结果：

markdown
# 代码审查报告

## 总体评价
[简要总结代码的整体质量和主要发现]

## 🔴 必须修复的问题
[列出必须解决的关键问题]

## 🟡 强烈建议改进
[列出重要的改进建议]

## 🟢 可选建议
[列出次要的优化建议]

## 👍 优点
[认可好的实践和亮点]

## 详细审查意见
[按代码区块或文件组织的详细评论]

## 下一步建议
[关于如何处理反馈的指导]

常见审查场景

场景1：审查小型PR（<200行）

  • 快速浏览整体结构
  • 重点检查功能正确性和安全性
  • 确保有基本测试
  • 提供简洁的反馈

场景2：审查大型PR（>400行）

  • 建议作者拆分PR
  • 先审查核心逻辑和关键路径
  • 可以要求作者提供设计文档或演示
  • 分阶段进行审查

场景3：审查初级开发者代码

  • 更加耐心和详细
  • 多提供示例和学习资源
  • 解释"为什么"而不只是"怎么做"
  • 多给予鼓励和肯定

场景4：安全关键代码

  • 仔细检查所有输入验证
  • 检查认证和授权逻辑
  • 检查敏感数据处理
  • 考虑使用自动化安全扫描工具

场景5：性能关键代码

  • 分析算法复杂度
  • 检查数据库查询效率
  • 考虑并发和资源使用
  • 建议性能测试和基准测试

反馈指导原则

建设性与尊重

  • 专注于代码，而非编写者：说"这个函数可以更简洁"而不是"你写得太复杂了"
  • 保持专业和尊重：即使发现严重问题，也要以建设性的方式提出
  • 提供具体建议：不要只说"这不好"，要说明为什么不好以及如何改进
  • 认可好的实践：发现优秀代码时也要给予肯定

清晰与具体

  • 明确问题所在：指出具体的行号和代码段
  • 说明影响：解释问题可能导致的后果（安全、性能、可维护性等）
  • 提供上下文：如果引用外部资源或标准，提供链接
  • 举例说明：必要时提供代码示例展示建议的改进方式

最佳实践

审查者

  1. 保持客观：专注于代码，而非编写者
  2. 及时审查：不要让PR长时间等待
  3. 完整审查：不要只看表面就批准
  4. 建设性沟通：提供解决方案，不只是指出问题
  5. 持续学习：从审查中学习新技术和模式

代码作者

  1. 自审：提交前先自己审查一遍
  2. 小而专注：保持PR小而专注于单一任务
  3. 清晰描述：写清楚PR的目的、改动和测试
  4. 响应及时：及时回应审查意见
  5. 开放心态：虚心接受建设性反馈

工具推荐

配合使用以下自动化工具可以提高审查效率：

  • 静态分析：SonarQube、ESLint、Pylint、RuboCop
  • 安全扫描：Snyk、OWASP Dependency-Check、GitGuardian
  • 代码格式：Prettier、Black、gofmt
  • 测试覆盖：Codecov、Coveralls、JaCoCo
  • 代码复杂度：CodeClimate、Radon

自动化工具可以处理格式、风格等基础问题，让审查者专注于逻辑、架构和设计。

When to Apply

应用此Skill当用户：

  • 明确请求代码审查："帮我审查这段代码"、"请检查这个PR"
  • 询问代码质量："这段代码有什么问题吗"、"如何改进这段代码"
  • 提到代码审查相关术语："code review"、"pull request review"
  • 上传代码文件并寻求反馈或改进建议
  • 询问代码是否遵循最佳实践
  • 需要安全审查或性能评估

Resources

此Skill包含以下参考资源：

REVIEW-CHECKLIST.md

详细的代码审查检查清单，包含8大类检查项：

  • 代码功能与逻辑
  • 代码质量（可读性、DRY、SOLID原则）
  • 安全性（认证、输入验证、数据保护）
  • 性能（算法、数据库、并发）
  • 测试覆盖
  • 文档
  • 架构与设计
  • 可维护性

使用时机：当需要进行全面、系统的代码审查时，参考此文档确保不遗漏任何重要检查项。

FEEDBACK-GUIDELINES.md

建设性反馈指南，涵盖：

  • 反馈原则（建设性、清晰、优先级分级）
  • 反馈模式（提问式、建议式、解释式、赞赏式）
  • 常见场景的反馈示例
  • 应该避免的反馈方式
  • 响应作者反馈的方法
  • 特殊场景处理

使用时机：当需要撰写PR评论或提供反馈时，参考此文档以确保反馈专业、建设性且有帮助。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Code Review AI skill do?

专业的代码审查助手，提供结构化的审查流程、详细的检查清单和建设性的反馈指南。适用于审查Pull Request、代码提交或任何需要代码质量评估的场景，涵盖功能性、安全性、性能、测试、文档、架构设计等多个维度。

Why use Code Review on TypingMind?

Because you install it once and use it with any model. Code Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/smallnest/langgraphgo/tree/master/testdata/skills/code-review/code-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Code Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Code Review?

As many as you like. As long as a model supports skills, you can use Code Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Code Review AI skill free?

Yes. It is published on GitHub by smallnest under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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