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deep-review深度回顾

Agent Skill

deep-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

1,999

周安装

85

GitHub Stars

1,678

下载量

843
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:deep-review(深度回顾)
来源仓库:https://github.com/coder/mux
仓库路径:skills/deep-review
安装命令:
npx skills add https://github.com/coder/mux --skill deep-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/coder/mux --skill deep-review

简介

deep-review 采用并行子代理机制执行高质量代码审查,覆盖多维度关注点。

  • 适用于 Pull Request 变更分析,重点检查正确性、测试、一致性与安全性。
  • 自动识别 UI、后端与 IPC 层变动,生成分层评审意见与修复指引。
  • 安装前应确认是否具备读取 diff 与项目结构图的权限。
  • 建议结合 git 历史与分支策略理解变更意图,避免孤立评判片段代码。

SKILL.md

Deep Review Mode

Provide an excellent code review by defaulting to parallelism.

You should use sub-agents to review the change from multiple angles (correctness, tests, consistency, UX, performance, safety). Each sub-agent should have a focused mandate and return actionable findings with file paths.

Step 0: Establish the review surface

Before reviewing, gather context:

  • Identify the change scope: git diff --name-only (or the file list the user provides).
  • Skim the diff for intent and risk: git diff.
  • Note which layers are touched:

- UI (React/components/styles) - Main process / backend services - IPC boundary / shared types - Tooling/scripts - Docs - Tests

If the change is large, split review by module and prioritize high-risk paths.

Spawn the right sub-agents (change-type aware)

Spawn 2–5 sub-agents depending on scope. Tailor them to the change.

Suggested sub-agent set

  • Correctness & edge cases (always)

- Goal: find logic bugs, missing error handling, race conditions, broken invariants.

  • Tests & verification (always)

- Goal: evaluate test coverage, propose missing tests, suggest commands to validate.

  • Consistency & architecture (usually)

- Goal: ensure changes match existing patterns, abstractions, and boundaries.

  • UX & accessibility (when UI changed)

- Goal: keyboard flows, a11y, visual consistency, empty/loading/error states.

  • Performance & reliability (when hot paths / streaming / IO changed)

- Goal: latency, unnecessary work, blocking calls, memory growth, resilience.

  • Docs & developer experience (when docs/scripts/public API changed)

- Goal: clarity, correctness, navigation updates, link integrity.

Synthesize into a single excellent review

When sub-agent results arrive, produce a consolidated review with:

  1. Summary (what changed + overall risk)
  2. Issues
  3. Questions (unknown intent; ask for clarification)
  4. Suggested validation plan (commands + manual checks)

Issues should have a severity in form of:

| Severity | Description | Example | |----------|-------------| | P0 | Change must not be merged until resolved | Change would permanently break core workflows if merged. | | P1 | Change should not be merged| New code will not work as expected due to severe bugs| | P2 | Consideration required before merging | The change creates inconsistency / fragility | | P3 | Minor issue | The change introduces a minor issue that may be addressed later | | P4 | Long-term issue | The change raises concerns about long-term maintainability or may break under rare conditions |

Review rubric

Use this rubric to avoid blind spots:

  • Correctness: invariants, edge cases, error handling, races
  • Fitness: does it meet the user goal, and does it match product constraints?
  • Tests: coverage of new logic, regression tests, deterministic behavior
  • Consistency: patterns, naming, types, boundaries, IPC typing
  • Maintainability: complexity, duplication, readability
  • Performance: hot paths, streaming, excessive re-renders/IO
  • Safety: secrets, path traversal, injection risks, filesystem safety
  • DX: logs, error messages, debuggability

Anti-patterns

  • Single-threaded review of a large change (spawn sub-agents).
  • Vague feedback (“looks good”) without actionable items and file paths.
  • Non-verifiable suggestions (always include a validation plan).
  • Scope creep disguised as review (focus on minimal changes unless risk demands more).

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

38.13%
按下载量换算321

Claude

32.33%
按下载量换算273

Cursor

17.44%
按下载量换算147

Gemini CLI

8.62%
按下载量换算73

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/coder/mux --skill deep-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。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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