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Requesting Code Review

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jnMetaCode
requesting-code-review

完成任务、实现重要功能或合并前使用,用于验证工作成果是否符合要求

Overview

PublisherjnMetaCode
Repositorysuperpowers-zh
Skill namerequesting-code-review
Stars
8.1K
Forks
758
Bundled files
1
LicenseMIT
Links
  • 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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Requesting 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/jnMetaCode/superpowers-zh.git /tmp/superpowers-zh
mkdir -p .claude/skills
cp -r /tmp/superpowers-zh/skills/requesting-code-review .claude/skills/requesting-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requesting 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 Requesting 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 Requesting 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.

请求代码审查

派遣代码审查子代理,在问题扩散之前发现它们。审查者获得的是精心组织的评估上下文——绝不是你的会话历史。

核心原则: 早审查,勤审查。

何时请求审查

必须审查:

  • 子代理驱动开发中每个任务完成后
  • 完成重要功能后
  • 合并到 main 之前

可选但有价值:

  • 卡住时(换个视角)
  • 重构之前(建立基线)
  • 修复复杂 bug 之后

如何请求

1. 获取 git SHA:

bash
BASE_SHA=$(git rev-parse HEAD~1)  # 或 origin/main
HEAD_SHA=$(git rev-parse HEAD)

2. 派遣代码审查子代理:

使用 Task 工具,指定 general-purpose 类型,填写 code-reviewer.md 中的模板

占位符说明:

  • {DESCRIPTION} - 你刚完成的内容简要说明
  • {PLAN_OR_REQUIREMENTS} - 预期功能
  • {BASE_SHA} - 起始提交
  • {HEAD_SHA} - 结束提交

3. 处理反馈:

  • Critical 问题立即修复
  • Important 问题在继续之前修复
  • Minor 问题记录下来稍后处理
  • 如果审查者有误,用技术理由反驳

示例

[刚完成任务 2:添加验证功能]

你:让我在继续之前请求代码审查。

BASE_SHA=$(git log --oneline | grep "Task 1" | head -1 | awk '{print $1}')
HEAD_SHA=$(git rev-parse HEAD)

[派遣代码审查子代理]
  DESCRIPTION: 添加了 verifyIndex() 和 repairIndex(),支持 4 种问题类型
  PLAN_OR_REQUIREMENTS: docs/superpowers/plans/deployment-plan.md 中的任务 2
  BASE_SHA: a7981ec
  HEAD_SHA: 3df7661

[子代理返回]:
  优点:架构清晰,测试真实
  问题:
    Important:缺少进度指示器
    Minor:报告间隔使用了魔法数字 (100)
  评估:可以继续

你:[修复进度指示器]
[继续任务 3]

常见的合理化借口

借口现实
"我自己看一下 diff 就行了,不用专门派审查者"你是协调者——在自己的会话里读 diff 会烧掉你继续推进工作所需的上下文窗口。派一个审查子智能体:diff 和评估过程都待在它的上下文里,只有结论回到你这里。
"审查者需要我的全部会话历史才能理解这次改动"给它精心组织的上下文,绝不给会话历史。这样审查者才会盯着工作成果,而不是你的思考过程。

红线

绝不要:

  • 因为"很简单"就跳过审查
  • 忽略 Critical 问题
  • 带着未修复的 Important 问题继续推进
  • 对合理的技术反馈进行争辩

如果审查者有误:

  • 用技术理由反驳
  • 展示证明其可行的代码/测试
  • 要求澄清

参见模板:requesting-code-review/code-reviewer.md

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 Requesting Code Review AI skill do?

完成任务、实现重要功能或合并前使用,用于验证工作成果是否符合要求

Why use Requesting Code Review on TypingMind?

Because you install it once and use it with any model. Requesting 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 Requesting Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jnMetaCode/superpowers-zh/tree/main/skills/requesting-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 Requesting 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 Requesting Code Review?

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

Is the Requesting Code Review AI skill free?

Yes. It is published on GitHub by jnMetaCode 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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