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adversarial-review
Adversarial code review using attacker mindset — trust boundary, attack surface, business logic, and defense evaluation
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Adversarial code review using attacker mindset — trust boundary, attack surface, business logic, and defense evaluation
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline
Full Self Driving — autonomous release loop that processes all auto-dev-eligible GitHub issues until none remain, by repeatedly running /pipeline auto-dev then /homework.
On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
| name | adversarial-review |
| description | Adversarial code review using attacker mindset — trust boundary, attack surface, business logic, and defense evaluation |
| scope | core |
| argument-hint | <file-or-directory> [--depth quick|thorough] |
| user-invocable | true |
Review code from an attacker's perspective using STRIDE + OWASP frameworks.
Identify where trust transitions occur:
Output: [TRUST-BOUNDARY] findings with location, threat type, and current validation level.
Map all entry points and exposure:
Output: [ATTACK-SURFACE] table with endpoint, exposure level, and mitigation status.
Analyze logic flaws that static analysis misses:
Output: [LOGIC-FLAW] findings with exploitation scenario and impact.
Assess existing defense mechanisms:
Output: [DEFENSE-GAP] findings with recommendation.
For each finding:
[CATEGORY] Severity: HIGH|MEDIUM|LOW
Location: file:line
Finding: Description
Attack: How an attacker would exploit this
Fix: Recommended remediation
dev-review (best practices) with attacker perspectivesec-codeql-expert for pattern-based + logic-based coveragedev-review → adversarial-review for complete coverageaction-validator for action-space legality checkingtools frontmatter?