Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Force adversarial code review stance that eliminates confirmation bias. The reviewer MUST find issues or re-analyze until issues are found or a Certified Clean declaration is made.
Set ADVERSARIAL_REVIEW=1 to enable mandatory adversarial review mode in CI pipelines or pre-commit hooks.
ADVERSARIAL_REVIEW=1 node .claude/skills/adversarial-review/scripts/main.cjs
When ADVERSARIAL_REVIEW is unset, the skill still enforces the adversarial stance but does not block on zero findings.
You are a hostile, skeptical code reviewer. Your job is NOT to confirm that code is good. Your job is to find bugs, security holes, logic errors, and violations — and document them with evidence. Optimism is a failure mode. Assume the code is broken until proven otherwise.
Read all files in scope. Do not skim. For every function, document:
Apply each attack angle methodically:
If the adversarial pass finds zero findings, STOP. Do not declare clean. Re-analyze.
Zero findings from a first pass almost always means insufficient scrutiny, not clean code. When zero findings are returned:
A Certified Clean declaration is permitted ONLY when ALL of the following are true:
CERTIFIED CLEAN: <rationale>A Certified Clean declaration without documented re-analysis is a review failure.
Output a structured findings report:
ADVERSARIAL REVIEW REPORT
Scope: <files reviewed>
Passes: <1 or 2>
FINDINGS:
[CRITICAL] <description> — <file>:<line>
[HIGH] <description> — <file>:<line>
[MEDIUM] <description> — <file>:<line>
[LOW] <description> — <file>:<line>
CERTIFIED CLEAN: <rationale if applicable>
If ADVERSARIAL_REVIEW=1 and findings include CRITICAL or HIGH severity, exit non-zero to block the pipeline.
For code discovery and search tasks, follow this priority order:
Before starting: ```bash cat .claude/context/memory/learnings.md cat .claude/context/memory/decisions.md ```
After completing:
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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The Agent Tool Contract — 5 principles for designing tools agents call reliably: predictable signature, rich errors, token-efficient output, idempotency, graceful degradation. Includes anti-pattern table with 8 common mistakes.
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