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differential-review差别审查

Agent Skill

differential-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

62,841

周安装

2,542

GitHub Stars

4,899

下载量

20,197
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

3

许可证

MIT

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:differential-review(差别审查)
来源仓库:https://github.com/trailofbits/skills
仓库路径:skills/differential-review
安装命令:
npx skills add https://github.com/trailofbits/skills --skill differential-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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/trailofbits/skills --skill differential-review

简介

通过自适应深度、爆炸半径计算和降价报告对代码更改进行以安全为中心的差异分析。

  • 分析三种代码库大小(小/中/大)的 PR、提交和差异,并以风险优先的方式优先考虑身份验证、加密、价值转移和外部调用
  • 包括六阶段工作流程:分类、代码分析、测试覆盖率评估、爆炸半径计算、对抗性建模和综合 Markdown 报告生成
  • 通过 git 历史分析检测安全回归,通过指责识别已删除的安全代码,并标记高风险模式,例如验证删除或访问控制更改
  • 与基线上下文的审计上下文构建和正式审计报告转换的问题作者集成
  • 提供明确的覆盖范围和置信水平;危险信号升级,例如高爆炸半径更改或删除的安全提交

SKILL.md

Differential Security Review

Security-focused code review for PRs, commits, and diffs.

Core Principles

  1. Risk-First: Focus on auth, crypto, value transfer, external calls
  2. Evidence-Based: Every finding backed by git history, line numbers, attack scenarios
  3. Adaptive: Scale to codebase size (SMALL/MEDIUM/LARGE)
  4. Honest: Explicitly state coverage limits and confidence level
  5. Output-Driven: Always generate comprehensive markdown report file

Rationalizations (Do Not Skip)

RationalizationWhy It's WrongRequired Action
"Small PR, quick review"Heartbleed was 2 linesClassify by RISK, not size
"I know this codebase"Familiarity breeds blind spotsBuild explicit baseline context
"Git history takes too long"History reveals regressionsNever skip Phase 1
"Blast radius is obvious"You'll miss transitive callersCalculate quantitatively
"No tests = not my problem"Missing tests = elevated risk ratingFlag in report, elevate severity
"Just a refactor, no security impact"Refactors break invariantsAnalyze as HIGH until proven LOW
"I'll explain verbally"No artifact = findings lostAlways write report

Quick Reference

Codebase Size Strategy

Codebase SizeStrategyApproach
SMALL (<20 files)DEEPRead all deps, full git blame
MEDIUM (20-200)FOCUSED1-hop deps, priority files
LARGE (200+)SURGICALCritical paths only

Risk Level Triggers

Risk LevelTriggers
HIGHAuth, crypto, external calls, value transfer, validation removal
MEDIUMBusiness logic, state changes, new public APIs
LOWComments, tests, UI, logging

Workflow Overview

Pre-Analysis → Phase 0: Triage → Phase 1: Code Analysis → Phase 2: Test Coverage
    ↓              ↓                    ↓                        ↓
Phase 3: Blast Radius → Phase 4: Deep Context → Phase 5: Adversarial → Phase 6: Report

Decision Tree

Starting a review?

├─ Need detailed phase-by-phase methodology?
│  └─ Read: methodology.md
│     (Pre-Analysis + Phases 0-4: triage, code analysis, test coverage, blast radius)
│
├─ Analyzing HIGH RISK change?
│  ├─ Read: adversarial.md
│  │  (Phase 5: Attacker modeling, exploit scenarios, exploitability rating)
│  └─ Or delegate to: adversarial-modeler agent
│     (Autonomous attacker modeling with concrete exploit scenarios)
│
├─ Writing the final report?
│  └─ Read: reporting.md
│     (Phase 6: Report structure, templates, formatting guidelines)
│
├─ Looking for specific vulnerability patterns?
│  └─ Read: patterns.md
│     (Regressions, reentrancy, access control, overflow, etc.)
│
└─ Quick triage only?
   └─ Use Quick Reference above, skip detailed docs

Agents

adversarial-modeler — Models attacker perspectives and builds exploit scenarios for HIGH RISK code changes. Follows the 5-step adversarial methodology (attacker model, attack vectors, exploitability rating, exploit scenario, baseline cross-reference) and produces structured vulnerability reports. Delegate to this agent when Phase 5 analysis is needed on high-risk changes.


Quality Checklist

Before delivering:

  • All changed files analyzed
  • Git blame on removed security code
  • Blast radius calculated for HIGH risk
  • Attack scenarios are concrete (not generic)
  • Findings reference specific line numbers + commits
  • Report file generated
  • User notified with summary

Integration

audit-context-building skill:

  • Pre-Analysis: Build baseline context
  • Phase 4: Deep context on HIGH RISK changes

issue-writer skill:

  • Transform findings into formal audit reports
  • Command: issue-writer --input DIFFERENTIAL_REVIEW_REPORT.md --format audit-report

Example Usage

Quick Triage (Small PR)

Input: 5 file PR, 2 HIGH RISK files
Strategy: Use Quick Reference
1. Classify risk level per file (2 HIGH, 3 LOW)
2. Focus on 2 HIGH files only
3. Git blame removed code
4. Generate minimal report
Time: ~30 minutes

Standard Review (Medium Codebase)

Input: 80 files, 12 HIGH RISK changes
Strategy: FOCUSED (see methodology.md)
1. Full workflow on HIGH RISK files
2. Surface scan on MEDIUM
3. Skip LOW risk files
4. Complete report with all sections
Time: ~3-4 hours

Deep Audit (Large, Critical Change)

Input: 450 files, auth system rewrite
Strategy: SURGICAL + audit-context-building
1. Baseline context with audit-context-building
2. Deep analysis on auth changes only
3. Blast radius analysis
4. Adversarial modeling
5. Comprehensive report
Time: ~6-8 hours

When NOT to Use This Skill

  • Greenfield code (no baseline to compare)
  • Documentation-only changes (no security impact)
  • Formatting/linting (cosmetic changes)
  • User explicitly requests quick summary only (they accept risk)

For these cases, use standard code review instead.


Red Flags (Stop and Investigate)

Immediate escalation triggers:

  • Removed code from "security", "CVE", or "fix" commits
  • Access control modifiers removed (onlyOwner, internal → external)
  • Validation removed without replacement
  • External calls added without checks
  • High blast radius (50+ callers) + HIGH risk change

These patterns require adversarial analysis even in quick triage.


Tips for Best Results

Do:

  • Start with git blame for removed code
  • Calculate blast radius early to prioritize
  • Generate concrete attack scenarios
  • Reference specific line numbers and commits
  • Be honest about coverage limitations
  • Always generate the output file

Don't:

  • Skip git history analysis
  • Make generic findings without evidence
  • Claim full analysis when time-limited
  • Forget to check test coverage
  • Miss high blast radius changes
  • Output report only to chat (file required)

Supporting Documentation


For first-time users: Start with methodology.md to understand the complete workflow.

For experienced users: Use this page's Quick Reference and Decision Tree to navigate directly to needed content.

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

24.71%
按下载量换算4,991

Codex

21.14%
按下载量换算4,270

OpenCode

18.64%
按下载量换算3,765

Gemini CLI

13.66%
按下载量换算2,759

Cursor

7.03%
按下载量换算1,420

Antigravity

3.73%
按下载量换算753

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。

来源信息

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