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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,权限为执行命令;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 71,596。

总安装 194周安装 8GitHub Stars 公开资料未说明下载 63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/buhond/skills
仓库路径:skills/code-review
安装命令:
npx skills add https://github.com/buhond/skills --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/buhond/skills --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 采用双代理架构分离评审与实施职责,确保客观公正性。适用于追求极致代码质量的团队,特别关注简洁性与必要性判断。评审代理完全独立于原开发上下文,从零开始评估所有技术选择合理性。支持打分制质量门禁,低于阈值则阻止合并直至问题彻底解决。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

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/buhond/skills --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

    它需要执行命令,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/buhond/skills --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?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/buhond/skills --skill code-review 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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 194 次,近一周安装 8 次,下载 63 次。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

  • 需要执行命令,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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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

194—执行命令未明确通过

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

*"Simplicity is the ultimate sophistication."* — Leonardo da Vinci

Two roles: a reviewer (always a fresh subagent) judges the code, and the implementing agent (you) applies fixes.

When To Use

  • User asks for a review or code review.
  • User asks for feedback on code quality or design.
  • User asks to simplify code or remove unnecessary abstraction.
  • User asks for a scored quality gate before merge.

Workflow

  1. Spawn a dedicated reviewer subagent. Use runSubagent, spawn_agent, or whatever the platform provides (Claude, Codex, Copilot, etc.). The reviewer starts from fresh context, isolated from the implementation. Use fork_context: false unless missing context would invalidate the review. If delegation is unavailable, tell the user — do not silently self-review.
  2. Give the reviewer only the changed files and the minimum context to understand them.
  3. The reviewer scores against the rubric, applies the review rules, and returns the quality score block.
  4. Surface the reviewer's result verbatim before acting on it — the user must see reviewer findings separately from fixer actions.
  5. If the verdict is fail, the implementing agent treats minimal_fix_plan as a mandate and applies fixes with craft and care. Then resubmit to the reviewer.
  6. After two failed cycles, stop and surface the unresolved choice clearly instead of iterating blindly.
  7. Stop when the review passes or the user interrupts.

Quality Score

The reviewer returns:

score: X/100
verdict: pass|fail
metrics:
  simplicity: X/30
  abstraction_quality: X/30
  solid_design: X/15
  readability: X/25
findings:
  - [P0|P1|P2|P3] file:line — issue and why it matters
minimal_fix_plan:
  - fix 1
  - fix 2

No findings → findings: - none, minimal_fix_plan: - none.

For P0/P1 architectural findings, append:

decision_memo:
  current_design: ...
  root_flaw: ...
  smallest_better_structure: ...
  why_now: ...

Scoring

CategoryWeight
Simplicity and minimal code30
Abstraction necessity and cohesion30
SOLID and dependency direction15
Readability and naming clarity25

pass requires score ≥ 90 with no automatic fail triggered.

Severity

  • P0: incorrect architecture or behavior with regression risk.
  • P1: design flaw that should block merge.
  • P2: meaningful maintainability or clarity issue.
  • P3: minor issue that does not threaten the design.

Automatic Fail

Any of these forces fail regardless of score:

  • Abstraction added without present need.
  • More code, layers, or state than the feature requires.
  • Duplicate logic left when consolidation is straightforward.
  • Symptom-level patch that leaves the root design smell intact.
  • Tests do not cover changed behavior.
  • Review does not explain the central design tradeoff.

Review Rules

These govern the reviewer's judgment. Each principle stated once.

Scope: review only the current diff and the code quality of the behavior it implements. Do not invent future requirements or demand unrelated cleanup. Treat behavior changes as intentional unless they create internal contradictions, unnecessary complexity, unclear ownership, or broken code-level contracts inside the changed design. Prefer one root cause over multiple symptoms of the same flaw. A change is non-trivial when it touches multiple modules, reshapes a boundary, adds a shared abstraction, changes state ownership, or changes dependency direction — review architecture before implementation details for these.

Priorities: architectural and code-quality risks over style. Concrete file/line findings over general advice. Root-cause issues before surface-level ones.

Simplicity: treat added complexity as guilty until proven necessary. Penalize abstractions that don't remove real, present duplication or coupling. Prefer deletion over addition when behavior stays correct. Reward clarity and low maintenance cost, not cleverness. Apply SOLID only when it reduces coupling and cognitive load, not as ceremony.

Architecture: behavior belongs in the layer that owns the decision. Details depend on policies, never the reverse. Reject abstractions that mix orchestration with mechanics. One source of truth per decision, dependency, and state transition. Prefer the smallest coherent implementation that works today.

Fix plan: minimal and tied directly to findings. For architectural work, "minimal" means the smallest change that restores correct ownership and dependency direction — not the smallest diff. It is fine to state a flaw without knowing the perfect rewrite, but the finding must point toward the fix direction.

Calibration: review as if matching the user's instincts about code smell, not a generic style guide. Default to suspicion when code feels AI-shaped or prematurely generalized.

Implementing Agent Standards

When the review fails, the implementing agent applies fixes with:

  • Craft: every change leaves the codebase better than found.
  • Creativity: make the problem dissolve, don't just address symptoms.
  • Quality: no shortcuts — the fix reads as if written on a senior engineer's best day.
  • Ownership: internalize the reviewer's bar. Treat findings as your own standards.

平台分布

Codex

38.07%
按下载量换算24

Claude

28.8%
按下载量换算18

Cursor

21.03%
按下载量换算13

Gemini CLI

9.85%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/buhond/skills --skill code-review 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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