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Agent Skill · 研究检索

code-review(代码审查)

code-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

code-review 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 64,379。

总安装 303周安装 13GitHub Stars 2下载 106核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:code-review(代码审查)
来源仓库:https://github.com/akillness/oh-my-gods
仓库路径:skills/code-review
安装命令:
npx skills add https://github.com/akillness/oh-my-gods --skill code-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/akillness/oh-my-gods --skill code-review

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于代码审查与高信号问题识别,适合 Pull Request 合并前的质量把关。适用于发现正确性缺陷、权限边界错误或缺失测试覆盖的情况。使用时需聚焦关键风险点(如状态变更、外部依赖),而非泛化风格检查。

安装命令:npx skills add https://github.com/akillness/oh-my-gods --skill code-review。应输出风险排序清单而非笼统评价,帮助开发者快速定位核心问题。

来源仓库内路径:skills/code-review

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/akillness/oh-my-gods --skill code-review;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

code-review 怎么安装?

在终端执行 npx skills add https://github.com/akillness/oh-my-gods --skill code-review。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

code-review 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

code-review 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

code-review 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

code-review 用的人多吗?

公开数据是累计安装 303 次,近一周安装 13 次,下载 106 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

code-review 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
code-review

代码审查 · 本页

3032接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Code Review

Code review is a judgment surface, not a generic style pass. Optimize for the highest-signal findings: correctness bugs, behavioral regressions, trust-boundary mistakes, performance risks, and missing verification.

When to use this skill

  • Review a pull request, diff, merge request, or patch stack before merge
  • Decide whether a change should be approved, blocked, or sent back for fixes
  • Identify the most important bugs, regressions, or missing tests in a change
  • Turn vague "please review this" requests into a risk-ranked findings list
  • Check whether the stated intent of a change matches what the diff actually does

Prefer a neighboring skill when the main job is not reviewing a concrete code change:

  • testing-strategies for deciding validation policy before tests exist
  • debugging for reproducing and isolating a known failure
  • security-best-practices for broader hardening guidance without a concrete diff
  • performance-optimization for measurement-led tuning work rather than review judgment

Instructions

Step 1: Establish review context

Start by pinning down:

  • change intent: feature, bug fix, refactor, migration, dependency update, or rollout change
  • blast radius: how many files or subsystems changed and whether critical paths moved
  • verification evidence: tests, typecheck, lint, screenshots, or manual checks already provided
  • risk class: auth, data integrity, billing, API compatibility, performance, or operational safety

If the request has no diff, patch, or code artifact to inspect, do not fake a review. Route to a better-fit planning or analysis skill instead.

Step 2: Look for findings before style comments

Prioritize this order:

  1. correctness and regression risk
  2. security and trust-boundary mistakes
  3. data-loss or compatibility risk
  4. missing or weak verification
  5. maintainability issues that are likely to cause future bugs

Do not lead with formatting or naming unless the change is otherwise clean.

Read references/review-priorities.md when the diff is large or the important findings are hard to rank.

Step 3: Inspect the diff by failure mode

Check the change against these questions:

  • Does the implementation actually satisfy the stated intent?
  • Did control flow, error handling, or cleanup behavior change in a risky way?
  • Are there hidden behavior changes at boundaries such as auth, persistence, API schemas, caching, concurrency, or background jobs?
  • Did the change add new assumptions without validation, fallback handling, or migration support?
  • Does the test evidence prove the risky paths, or only the happy path?

Prefer concrete evidence over generic checklists. A small focused finding beats ten broad reminders.

Step 4: Treat missing verification as a review issue

Review is not complete just because the code "looks fine."

  • If the risky behavior is untested, say so explicitly
  • Distinguish missing merge-blocking evidence from nice-to-have follow-up coverage
  • Tie the missing test request to a concrete failure mode
  • Route policy questions to testing-strategies and implementation work to the relevant testing skill

Read references/findings-format-and-severity.md when deciding whether to block on missing tests or weaker evidence.

Step 5: Produce a findings-first review

Expected response order:

  1. Findings — ordered by severity, each with file/line references when possible
  2. Open questions / assumptions — only if they materially affect correctness
  3. Change summary — brief and secondary

Each finding should include:

  • severity: critical, high, medium, or low
  • what is wrong or risky
  • why it matters in behavior, not only in style
  • what evidence is missing or what change would address it

If there are no material findings, say so explicitly and still mention any residual risk or verification gaps.

Output format

Expected response shape:

  • Findings: findings first, ordered by severity, with file/line references where possible
  • Open questions / assumptions: only unresolved issues that affect the review verdict
  • Change summary: brief recap of what the diff does
  • Residual risk: anything not fully proven by available verification

Examples

Example 1: Review a risky auth change

Input:

Review this PR that changes token refresh handling and session invalidation.

Expected shape:

  • focuses on auth and session-boundary regressions before style
  • checks whether failure and expiry paths are actually covered
  • calls out merge-blocking findings before summaries

Example 2: Review a broad refactor

Input:

Please review this refactor before I merge it. I mostly want to know if there are regression risks or missing tests.

Expected shape:

  • looks for behavior drift and hidden assumptions instead of naming-only feedback
  • treats missing regression coverage as a real review finding when warranted
  • keeps any summary short after the findings list

Example 3: Route away when there is no diff

Input:

Review this architecture direction for our checkout redesign.

Expected shape:

  • notes that this is not a code review because no concrete change set exists
  • routes to a planning, architecture, or design-analysis surface instead
  • does not pretend to produce diff findings without evidence

Best practices

  • Start from the diff's real risk, not a canned checklist.
  • Prefer a few concrete findings over a long list of weak comments.
  • Treat missing test evidence as part of review quality.
  • Keep findings behavior-focused and reference-backed.
  • Add eval coverage before any skill-autoresearch loop on this skill.
  • Move detailed heuristics into references so the entrypoint stays compact and triggerable.

References

  • Local: references/review-priorities.md
  • Local: references/findings-format-and-severity.md

平台分布

Codex

32.94%
按下载量换算35

Claude

29.86%
按下载量换算32

Cursor

18.51%
按下载量换算20

Gemini CLI

9.6%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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