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研究检索只读github未标认证来源可访问许可证需确认审计通过

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

250

周安装

10

GitHub Stars

2

下载量

81
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/preetamnath/agent-skills
仓库路径:skills/code-review
安装命令:
npx skills add https://github.com/preetamnath/agent-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/preetamnath/agent-skills --skill code-review

简介

代码审查技能用于查找、检索和筛选相关信息,支持关键词和场景匹配。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境。
  • 通过 GitHub 安装,使用 npx skills add 命令添加指定仓库的技能。
  • 需确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • code-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Code Review

Analyze code changes for correctness, security, edge cases, and quality. Return structured findings in the Finding schema v1.

When to use

  • Reviewing code changes (staged, unstaged, or specific commits)
  • Reviewing specific files or file sets
  • Quick structured review of a PR or branch

For full two-pass review with adversarial verification, use /two-pass-review instead.

Instructions

1 — Gather the artifact

Determine what to review:

  • If the user specified files: read those files
  • If the user specified a diff range: run git diff <range>
  • Otherwise: run git status and git diff to see current changes

For modified files, review the diff. For untracked files, read the full content. For deleted files, check for broken references.

If related files are needed for context (types, interfaces, callers), read them too.

If the scope is ambiguous (no files specified, no diff range, or the diff spans many unrelated files), use the AskUserQuestion tool to confirm what to review before proceeding. Present the detected files/changes and ask whether to review all or narrow to a subset.

2 — Automated checks

If the project supports it:

  • Read package.json to check available scripts
  • If a lint script exists, run it
  • If tsconfig.json exists, run npx tsc --noEmit
  • Note any failures as P0/P1 findings

3 — Analyze

Review the code for:

  • Correctness — does the code do what it's supposed to?
  • Security — injection, unauthorized access, data exposure?
  • Edge cases — unhandled scenarios, boundary conditions?
  • Bugs — obvious errors, off-by-one, null references?
  • Performance — inefficient patterns (only flag with evidence)?

Do NOT flag: style preferences, naming opinions, theoretical risks without evidence, or things you'd do differently but aren't wrong.

4 — Return findings

Return a ReviewOutput envelope conforming to the Output Schema below.

  • Set verdict and evidence to null on all findings
  • Include honest confidence scores — 1.0 means certain, below 0.5 means you're guessing
  • Populate checks_run with what you evaluated (files, criteria, lint/typecheck results)
  • If no issues are found, return an empty findings array — don't manufacture problems

Constraints

  • No fixes. Do NOT implement fixes unless explicitly asked.
  • Report only. Present findings for the user to review.

Output Schema

Finding

Finding {
  id: sequential number starting from 1,
  severity: "P0" | "P1" | "P2" | "P3",
  title: short title,
  body: detailed explanation with evidence,
  file: file path or null for global issues,
  line_start: number or null,
  line_end: number or null,
  confidence: 0.0-1.0,
  criterion: what was violated,
  verdict: "confirmed" | "demoted" | "rejected" | null,
  evidence: reasoning for verdict | null
}

ReviewOutput

Findings are wrapped in a ReviewOutput envelope:

ReviewOutput {
  schema_version: "v1",
  findings: Finding[],
  checks_run: string[]
}

Severity calibration

  • P0 — Must fix: breaks functionality, security breach, data loss, or violates criteria
  • P1 — Fix before shipping: correct but incomplete, fragile, or reliability risk
  • P2 — Should fix: quality issue, code smell, not blocking
  • P3 — Nice to have: observation, style, minor improvement

Field notes

  • confidence — 1.0 means certain, below 0.5 means you're guessing. Be honest.
  • criterion — required for P0/P1 findings. Name the specific criterion violated.
  • verdict — populated by the verifier in two-pass review. Set to null when producing findings directly.
  • evidence — verifier's reasoning for the verdict. Set to null when producing findings directly.
  • checks_run — list every criterion evaluated, file path checked, or acceptance criterion verified. For ACs, use AC-N: PASS — [evidence] or AC-N: FAIL — [reason].

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

37.44%
按下载量换算30

Claude

30.04%
按下载量换算24

Cursor

18.06%
按下载量换算15

Gemini CLI

9.21%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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