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Agent Skill · 研究检索

adversarial-review(对抗性审查)

adversarial-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。

adversarial-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 59,221。

总安装 392周安装 16GitHub Stars 32下载 127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:adversarial-review(对抗性审查)
来源仓库:https://github.com/steveclarke/dotfiles
仓库路径:skills/adversarial-review
安装命令:
npx skills add https://github.com/steveclarke/dotfiles --skill adversarial-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/steveclarke/dotfiles --skill adversarial-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。

adversarial-review 用于查找、检索和筛选相关信息,适合在安全审查或漏洞检测场景中快速定位内容。它适用于根据关键词、任务场景或来源线索提取候选结果,帮助 Agent 识别潜在风险。通过 npx skills add 命令从 GitHub 仓库安装,具体用法可参考原始 README 和 SKILL.md。

安装前需确认权限范围和维护状态,注意是否涉及联网、命令执行或文件读写操作。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/adversarial-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/steveclarke/dotfiles --skill adversarial-revie…;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

adversarial-review 怎么安装?

在终端执行 npx skills add https://github.com/steveclarke/dotfiles --skill adversarial-review。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

adversarial-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。

adversarial-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/steveclarke/dotfiles --skill adversarial-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。

adversarial-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。

adversarial-review 用的人多吗?

公开数据是累计安装 392 次,近一周安装 16 次,下载 127 次,GitHub 32 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。

adversarial-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
adversarial-review

对抗性审查 · 本页

39232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

Adversarial Review

Automated critique-fix loop using fresh-eyes reviewer subagents. Each round, a new subagent reviews the code cold — no knowledge of why decisions were made, no attachment to the implementation. The main agent fixes Critical and Suggestion findings, then a fresh reviewer checks again. Loop stops when only Nitpicks remain or after 3 rounds.

When to Use

  • After implementation, before committing — especially for features and refactors
  • When changes touch critical paths, security-sensitive code, or complex logic
  • When you want a review that's harder than what you'd give yourself
  • Position in pipeline: implement → /simplify → /adversarial-review → /finalize → commit

When NOT to Use

  • Trivial changes, docs-only, config tweaks — use /simplify + /finalize instead
  • Mid-implementation — finish building first, then review
  • When you just want a quick sanity check — use /code-review instead

Input Modes

Parse $ARGUMENTS to determine what to review:

  • No args: review uncommitted changes — run git diff and git diff --cached to get staged + unstaged changes
  • File/dir paths: review specific files — read them directly and present as the code to review
  • --pr flag: review the full branch diff — run git diff main...HEAD (try master if main doesn't exist)

If the diff is empty, tell the user there's nothing to review and stop.

Step 1: Gather Context

Before dispatching the reviewer, collect two things:

The diff

Collect the diff based on the input mode above. This is what the reviewer will critique.

Project conventions

Search the project for convention docs the reviewer should know about. Look for:

  • CLAUDE.md files (project root and nested directories)
  • Guide/convention docs in common locations: project/guides/, docs/, .cursor/rules/, AGENTS.md
  • Lint and style config files (.rubocop.yml, .eslintrc.*, .prettierrc, standardrb config, etc.)

Budget: Keep convention context to ~5K tokens. Include CLAUDE.md files and lint configs. Do NOT include full implementation guides, feature docs, or READMEs — the reviewer is reviewing code quality, not feature completeness. If convention material exceeds the budget, prioritize CLAUDE.md files first, then lint configs.

Step 2: Dispatch Reviewer Subagent

Use the Agent tool to spawn a reviewer:

  • subagent_type: general-purpose
  • description: "Adversarial code review — round N"
  • prompt: the full reviewer prompt below, with {conventions} and {diff} replaced with the actual content

Reviewer Prompt

You are a senior developer doing a cold code review. You have never seen this
code before. You have no context on why decisions were made. You did not write
any of this. Review with completely fresh eyes.

## Project Conventions

{conventions}

## Code to Review

{diff}

## Review Criteria

Be opinionated. Review for:

- **Correctness:** bugs, logic errors, edge cases, race conditions
- **Security:** injection, auth bypass, data exposure, OWASP top 10
- **DRY:** duplicated logic that should be extracted
- **Modularity:** classes/methods doing too much, unclear boundaries
- **Testability:** code that's hard to test, missing test coverage
- **Simplicity:** over-engineering, unnecessary abstractions, premature generalization
- **Architecture:** does it conform to the project's established patterns and conventions?
- **Naming:** unclear or misleading names
- **Error handling:** swallowed errors, missing edge cases

## Output Format

Return findings as a list. Each finding MUST have:

- **Severity:** Critical | Suggestion | Nitpick
- **Location:** file path and line range
- **Issue:** what's wrong
- **Fix:** specific, concrete recommendation

Severity definitions:
- **Critical** = bugs, security issues, data loss risk, broken functionality
- **Suggestion** = improvements that meaningfully affect quality, maintainability, or conformance
- **Nitpick** = style preferences, minor naming quibbles, cosmetic issues

Be thorough but honest about severity. Don't inflate Nitpicks to Suggestions.
If the code is solid, say so — an empty findings list is a valid result.

Step 3: Process Findings

When the reviewer subagent returns:

  1. Read through the findings
  2. Fix all Critical and Suggestion items directly in the code
  3. Skip any finding that conflicts with established project conventions or prior discussion in this session — note why you skipped it
  4. Log Nitpick items without acting on them
  5. Track what was found and fixed this round

Use your judgment. If a finding is wrong or would break something, skip it and note why. The reviewer has fresh eyes but lacks your conversation context — you know things it doesn't.

Step 4: Loop

After applying fixes:

  1. Collect the new diff — compare against the original baseline (not the previous round). The reviewer should see the full changeset each time, not just the fixes.
  2. Spawn a fresh reviewer subagent with the updated diff. Do not reuse the previous subagent.
  3. Repeat until one of these conditions is met:

- Only Nitpick findings remain → stop, the code is clean - 3 rounds completed → stop, report any remaining non-nitpick findings

Step 5: Report

Present a summary when done:

## Adversarial Review Complete

**Rounds:** N of 3
**Result:** [Clean — only nitpicks remain | Capped — N issues remain after 3 rounds]

### Round 1
- [Critical] file.rb:12-15 — description (FIXED)
- [Suggestion] file.rb:30 — description (FIXED)
- [Nitpick] file.rb:45 — description

### Round 2
- [Nitpick] file.rb:32 — description

### Remaining Nitpicks
- file.rb:45 — description
- file.rb:32 — description

If the first round returns no findings at all, report that the code passed clean and stop.

Common Mistakes

  • Reusing the same subagent for round 2 — it remembers its own findings and loses the "fresh eyes" benefit. Always spawn a new one.
  • Diffing fixes only instead of the full changeset — the round 2 reviewer should see everything, not just what changed since round 1.
  • Applying every finding blindly — you have conversation context the reviewer lacks. Use judgment. Skip findings that conflict with project conventions or prior decisions, and note why.
  • Running this on trivial changes — this is expensive. If you just renamed a variable, use /simplify + /finalize instead.

平台分布

Codex

32.52%
按下载量换算41

Claude

31.07%
按下载量换算39

Cursor

19.23%
按下载量换算24

Gemini CLI

9.83%
按下载量换算12

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/steveclarke/dotfiles --skill adversarial-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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