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code-review代码审查

Agent Skill

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。

总安装

303

周安装

13

GitHub Stars

2

下载量

106
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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