adversarial-skill-audit

Adversarial stress testing reveals skill quality gaps by applying multi-pass evaluations against skill-builder standards, exposing trigger failures, ambiguous instructions, and safety risks while generating per-skill scorecards and remediation plans.

Adversarial skill auditing is a formal evaluation framework that stress-tests AI skill directories by attacking frontmatter, instructions, structure, safety, and token efficiency to surface weaknesses and enforce ecosystem consistency.

Adversarial stress tests evaluate trigger gaps and ambiguous instructions by simulating risk scenarios and measuring instruction clarity against skill-builder quality standards to produce actionable scorecards.

Running a full adversarial audit generates per-skill scorecards, remediation plans, and traceable audit journal entries, ensuring directory-level coherence and actionable ecosystem improvements.

The skill auditing framework operates without external dependencies, utilizing internal scripts and references to perform multi-pass adversarial evaluations and enforce directory-level coherence.

Avoid adversarial skill auditing for basic syntax checks, as this approach enforces deep multi-pass stress testing across safety, token efficiency, and maintenance to reveal structural weaknesses and overlaps.