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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,权限为需要联网;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 45,550。

总安装 840周安装 34GitHub Stars 26下载 264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
MIT

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/outfitter-dev/agents
仓库路径:skills/code-review
安装命令:
npx skills add https://github.com/outfitter-dev/agents --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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/outfitter-dev/agents --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。

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。适用于代码审查规范查询、常见缺陷模式识别或评审流程优化等场景。通过关键词匹配返回审查清单、工具推荐或典型案例。

安装命令为 npx skills add https://github.com/outfitter-dev/agents --skill code-review。使用前请确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。

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/outfitter-dev/agents --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

    它需要需要联网,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/outfitter-dev/agents --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?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

code-review 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

code-review 用的人多吗?

公开数据是累计安装 840 次,近一周安装 34 次,下载 264 次,GitHub 26 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?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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

  • 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

84026需要联网MIT有提醒

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

Fresh Eyes Review

Systematic pre-commit quality gate → checklist-based review → findings → summary.

<when_to_use>

  • Pre-commit code review and quality gates
  • Pre-merge pull request reviews
  • Systematic code audits before deployment
  • Quality verification for critical changes
  • Second-opinion review requests

NOT for: quick sanity checks, trivial typo fixes, formatting-only changes

</when_to_use>

<announcement_protocol>

Starting Review

Review Scope: {files/areas under review} Focus Areas: {specific concerns or general quality gate} Checklist: {full or targeted categories}

During Review

Emit findings as discovered:

  • {SEVERITY} {FILE_PATH}:{LINE} — {issue description}
  • Impact: {consequences if shipped}
  • Fix: {concrete remediation}

Completing Review

Review Complete

Findings Summary:

  • ◆◆ Severe: {COUNT} — blocking issues
  • ◆ Moderate: {COUNT} — should fix before merge
  • ◇ Minor: {COUNT} — consider addressing

Recommendation: {ship / fix blockers / needs rework}

{detailed findings below if any found}

</announcement_protocol>

Type Safety

  • ✓ No any types without justification comment
  • ✓ Null/undefined handled explicitly (optional chaining, nullish coalescing)
  • ✓ Type guards used for union types
  • ✓ Discriminated unions for state machines
  • ✓ Generic constraints specified where needed
  • ✓ Return types explicit on public functions
  • ✓ No type assertions without safety comment

Error Handling

  • ✓ All error paths handled (no silent failures)
  • ✓ Meaningful error messages with context
  • ✓ Errors propagated or logged appropriately
  • ✓ Result types used for expected failures
  • ✓ Try/catch blocks have specific error handling
  • ✓ Promise rejections handled
  • ✓ Resource cleanup in finally blocks

Security

  • ✓ User input validated before use
  • ✓ No hardcoded secrets or credentials
  • ✓ Authentication/authorization checks present
  • ✓ Parameterized queries (no SQL injection)
  • ✓ XSS prevention (sanitized output)
  • ✓ CSRF protection where applicable
  • ✓ Sensitive data encrypted/hashed
  • ✓ Rate limiting on public endpoints

Testing

  • ✓ Tests exist for new functionality
  • ✓ Edge cases covered
  • ✓ Error scenarios tested
  • ✓ Actual assertions (not just execution)
  • ✓ No test pollution (proper setup/teardown)
  • ✓ Mocks used appropriately (not overused)
  • ✓ Test names describe behavior
  • ✓ Integration tests for critical paths

Code Quality

  • ✓ Names reveal intent (functions, variables, types)
  • ✓ Functions <50 lines (single responsibility)
  • ✓ Files <500 lines (consider splitting)
  • ✓ No magic numbers (use named constants)
  • ✓ DRY violations eliminated
  • ✓ Nested conditionals <3 deep
  • ✓ Cyclomatic complexity reasonable
  • ✓ Dead code removed

Documentation

  • ✓ Public APIs have JSDoc/TSDoc
  • ✓ Complex algorithms explained
  • ✓ Non-obvious decisions documented
  • ✓ Breaking changes noted
  • ✓ TODOs have context and owner
  • ✓ README updated if behavior changes
  • ✓ Examples provided for complex usage

Performance

  • ✓ No obvious N+1 queries
  • ✓ Appropriate data structures used
  • ✓ Unnecessary allocations avoided
  • ✓ Heavy operations async/batched
  • ✓ Caching where beneficial
  • ✓ Database indexes considered

Rust-Specific (when applicable)

  • ✓ rustfmt and clippy passing
  • ✓ Result preferred over panic
  • ✓ No unwrap/expect outside tests/startup
  • ✓ Ownership/borrowing idiomatic
  • ✓ Send/Sync bounds respected
  • ✓ Unsafe code justified with comments
  • ✓ Proper error types (thiserror/anyhow)

1. Announce (activeForm: Announcing review)

Emit starting protocol:

  • Scope of review
  • Focus areas
  • Checklist approach (full or targeted)

2. Checklist (activeForm: Running checklist review)

Systematically verify each category:

  • Type Safety → Error Handling → Security → Testing → Quality → Docs → Performance
  • Flag violations immediately with severity
  • Note clean areas briefly

3. Deep Dive (activeForm: Investigating findings)

For each finding:

  • Verify it's actually a problem (not false positive)
  • Assess severity and impact
  • Determine concrete fix
  • Check for pattern across codebase

4. Summarize (activeForm: Compiling review summary)

Emit completion protocol:

  • Findings count by severity
  • Recommendation (ship / fix blockers / rework)
  • Detailed findings list
  • Optional: patterns noticed, suggestions for future

Load the maintain-tasks skill for tracking review stages.

<finding_format>

{SEVERITY} {FILE_PATH}:{LINE_RANGE}

Issue: {clear description of problem}

Impact: {consequences if shipped — security risk, runtime error, maintenance burden, etc}

Fix: {concrete steps to remediate}

Pattern: {if issue appears multiple times, note scope}


Example:

◆◆ src/auth/login.ts:45-52

Issue: Password compared using == instead of constant-time comparison

Impact: Timing attack vulnerability — attacker can infer password length and content through response timing

Fix: Use crypto.timingSafeEqual() or bcrypt's built-in comparison

Pattern: Single occurrence


</finding_format>

<severity_guidance>

◆◆ Severe (blocking):

  • Security vulnerabilities
  • Data loss risks
  • Runtime crashes in common paths
  • Breaking changes without migration
  • Test failures or missing critical tests

◆ Moderate (should fix):

  • Type safety violations
  • Unhandled error cases
  • Poor error messages
  • Missing tests for edge cases
  • Significant code quality issues
  • Missing documentation for public APIs

◇ Minor (consider addressing):

  • Code style inconsistencies
  • Overly complex but functional code
  • Minor performance optimizations
  • Documentation improvements
  • TODOs without context
  • Naming improvements

</severity_guidance>

Loop: Scan → Verify → Document → Next category

  1. Announce review — scope, focus, approach
  2. Run checklist — systematically verify each category
  3. Document findings — severity, location, issue, impact, fix
  4. Investigate patterns — does finding repeat? Broader issue?
  5. Deep dive blockers — verify severity assessment, ensure fix is clear
  6. Compile summary — counts by severity, recommendation
  7. Deliver findings — completion protocol with detailed list

At each finding:

  • Verify it's actually a problem
  • Assess impact if shipped
  • Determine concrete fix
  • Note if pattern across files

Before completing review:

Check coverage:

  • ✓ All checklist categories verified?
  • ✓ Both happy path and error paths reviewed?
  • ✓ Tests examined for actual assertions?
  • ✓ Security-sensitive areas given extra scrutiny?

Check findings quality:

  • ✓ Severity accurately assessed?
  • ✓ Impact clearly explained?
  • ✓ Fix actionable and concrete?
  • ✓ False positives eliminated?

Check recommendation:

  • ✓ Aligned with findings severity?
  • ✓ Blockers clearly marked?
  • ✓ Path forward unambiguous?

ALWAYS:

  • Announce review start with scope and focus
  • Run systematic checklist, don't skip categories
  • Emit findings as discovered, don't batch at end
  • Assess severity honestly (err toward caution)
  • Provide concrete fixes, not just complaints
  • Complete with summary and recommendation
  • Mark false positives if checklist item doesn't apply
  • Consider patterns (single issue or systemic?)

NEVER:

  • Skip checklist review for "quick check"
  • Assume code is safe without verification
  • Flag style preferences as blockers
  • Provide vague findings without fix guidance
  • Approve severe findings "for later fix"
  • Complete review without announcement protocol
  • Miss security checks on user input paths
  • Ignore test quality (execution!= validation)

Core methodology:

  • checklist.md — extended checklist details, examples, severity guidance

Related skills:

  • codebase-recon — evidence-based investigation (foundation for review)
  • debugging — structured bug investigation

平台分布

Claude Code

28.22%
按下载量换算75

windsurf

24.34%
按下载量换算64

OpenCode

18.63%
按下载量换算49

Cursor

12.8%
按下载量换算34

Codex

9.55%
按下载量换算25

Antigravity

3.96%
按下载量换算10

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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