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review-code审查代码

Agent Skill

review-code 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

204

周安装

12

GitHub Stars

292

下载量

97
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:review-code(审查代码)
来源仓库:https://github.com/tobihagemann/turbo
仓库路径:skills/review-code
安装命令:
npx skills add https://github.com/tobihagemann/turbo --skill review-code
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/tobihagemann/turbo --skill review-code

简介

review-code 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Review Code

Review code against type-specific criteria. Runs internal reviews and /peer-review in parallel by default. Returns combined structured findings.

Types: correctness, security, api-usage, consistency, simplicity, coverage

With a type argument, runs a single-concern internal review plus the peer review. With no type argument, runs all six internal reviews plus the peer review.

Step 1: Determine the Scope

Determine what to review:

  • If a specific diff command was provided (e.g., git diff --cached, git diff main...HEAD), use that.
  • If a file list or directory was provided, review those files directly (read the full files, not a diff).
  • If neither was provided, default to diffing against the repository's default branch (detect via gh repo view --json defaultBranchRef --jq '.defaultBranchRef.name'). If there are no changes against the default branch, stop and state that there is nothing to review.

Step 2: Run Reviews in Parallel

Read the reference file(s) for the active type(s):

Full review activates all six types; a single-concern argument activates one. Skip peer review when instructed (e.g., "without peer review", "no peer", "internal only").

Use the Agent tool to launch all agents below in a single assistant message so they run concurrently. Each Agent call uses model: "opus" and does not set run_in_background. For full review that is seven Agent tool calls (six internal + one peer); for single-concern it is two (one internal + one peer).

  • Internal Agent (one per active type): Launch a separate Agent tool call for each active type. Pass the scope and the type's reference file content; the subagent applies the criteria and returns findings in the output format below.
  • Peer review Agent (unless skipping): Launch an Agent tool call whose prompt instructs the subagent to invoke /peer-review via the Skill tool with a request describing: (a) the scope to review; (b) each active type as a separate review dimension so they are reviewed independently; (c) for each dimension, the criteria live in ~/.claude/skills/review-code/references/<type>-review.md — the reviewer should read that file directly, use its priority scale and verdict label, and include any extra metadata fields it specifies (e.g., **Category:**, **Library:**, **Docs:**) between the **Reviewer:** line and the paragraph.

Aggregate findings with attribution (reviewer: "internal" or "peer"; type; file path). Present them in the output format below.

Then use the TaskList tool and proceed to any remaining task.

Output Format

Return findings as a numbered list. For each finding:

### [P<N>] <title (imperative, ≤80 chars)>

**File:** `<file path>` (lines <start>-<end>)
**Reviewer:** <internal | peer> (<type>)

<one paragraph explaining the issue and its impact>

The reference file may specify additional metadata fields (e.g., **Category:**, **Library:**, **Docs:**). Include them between the **Reviewer:** line and the paragraph.

After all findings, add an overall verdict per active type using the label from each reference file. For single-concern, that is one verdict block; for full review, six. After the per-type verdicts, add a single combined ## Peer Review Verdict block summarizing what the peer review returned.

## Overall Verdict — <type>

**<Verdict Label>:** <status>

<1-3 sentence assessment>

If there are no qualifying findings for a type, state so under that type's verdict block and explain briefly.

Rules

  • Present findings grouped by priority.
  • In full code review mode, present findings in file order to minimize context switching.

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

34.25%
按下载量换算33

Claude

28.94%
按下载量换算28

Cursor

20.08%
按下载量换算19

Gemini CLI

9.61%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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 warning/failed 结果,不能写成本站确认安全。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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