Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Attribution: Sourced from trailofbits/skills by Trail of Bits, licensed CC BY-SA 4.0. Modified for this collection.
Security-focused code review for PRs, commits, and diffs.
| Rationalization | Why It's Wrong | Required Action |
|---|---|---|
| "Small PR, quick review" | Heartbleed was 2 lines | Classify by RISK, not size |
| "I know this codebase" | Familiarity breeds blind spots | Build explicit baseline context |
| "Git history takes too long" | History reveals regressions | Never skip Phase 1 |
| "Blast radius is obvious" | You'll miss transitive callers | Calculate quantitatively |
| "No tests = not my problem" | Missing tests = elevated risk rating | Flag in report, elevate severity |
| "Just a refactor, no security impact" | Refactors break invariants | Analyze as HIGH until proven LOW |
| "I'll explain verbally" | No artifact = findings lost | Always write report |
| Codebase Size | Strategy | Approach |
|---|---|---|
| SMALL (<20 files) | DEEP | Read all deps, full git blame |
| MEDIUM (20-200) | FOCUSED | 1-hop deps, priority files |
| LARGE (200+) | SURGICAL | Critical paths only |
| Risk Level | Triggers |
|---|---|
| HIGH | Auth, crypto, external calls, value transfer, validation removal |
| MEDIUM | Business logic, state changes, new public APIs |
| LOW | Comments, tests, UI, logging |
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
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: differential-review: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
differential-review: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, passing that full namespaced name as subagent_type — a bare
adversarial-modeler is unregistered and the dispatch fails at runtime.
Before delivering:
audit-context-building skill:
issue-writer skill:
issue-writer --input DIFFERENTIAL_REVIEW_REPORT.md --format audit-reportInput: 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
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
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
For these cases, use standard code review instead.
Immediate escalation triggers:
These patterns require adversarial analysis even in quick triage.
Do:
Don't:
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.
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Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.