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adversarial-review对抗性审查

Agent Skill

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。

总安装

392

周安装

16

GitHub Stars

32

下载量

127
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: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

简介

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。

SKILL.md

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.

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

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。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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