adversarial-skill-audit

Audit AI agent skills against builder standards with adversarial stress tests.

19|6|Updated Sep 13, 2025

Search Tags:#templates #audit #safety #quality #skill #adversarial #instruction

Ready to run

npx skills add https://github.com/neverinfamous/memory-journal-mcp --skill adversarial-skill-audit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: adversarial-skill-audit
Source: https://github.com/neverinfamous/memory-journal-mcp/tree/main/skills/adversarial-skill-audit
Command: npx skills add https://github.com/neverinfamous/memory-journal-mcp --skill adversarial-skill-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Auditing and maintaining AI agent skill directories is error-prone without a formal, scalable process. This skill provides a multi-pass adversarial auditing framework that assesses each skill against the skill-builder quality standards, surfaces gaps, and enforces consistency across the collection.

Core Features & Use Cases

Multi-pass evaluation against skill-builder standards to measure frontmatter, triggering, instruction clarity, structure, safety, token efficiency, and maintenance.
Adversarial stress-testing to reveal trigger gaps, ambiguous instructions, and potential risk scenarios.
Generates per-skill scorecards, an improvement plan, and audit journal entries for traceability.
Enables directory-level coherence and ecosystem consistency checks to ensure a cohesive skills catalog.

Quick Start

Run a full adversarial audit on the target skills directory to identify gaps and actionable improvements.

Frequently Asked Questions about adversarial-skill-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I audit AI agent skills for quality and safety gaps?▼

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.

What is a multi-pass adversarial audit for skill directories?▼

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.

How do I check if my skill instructions have trigger gaps or ambiguous logic?▼

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.

Can I generate a remediation plan and traceable audit journal for my skills?▼

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

Does this skill auditing framework work without external dependencies?▼

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

When should I not use an adversarial approach to skill quality evaluation?▼

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.