Adversarial Skill Audit
SkillDocs & knowledgeMulti-pass adversarial quality audit for agent skill directories. Combines structured evaluation with adversarial stress-testing to assess skill completeness, instruction clarity, trigger accuracy, and security. Use when auditing a skills directory.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Adversarial Skill Audit skill
What this skill tells your AI
The instructions your AI receives, as published by neverinfamous/memory-journal-mcp in skills/adversarial-skill-audit/SKILL.md and read by ahel’s review.
A multi-pass quality auditing system for agent skill directories. Evaluates
each skill against the skill-builder quality standards through structured
profiling and adversarial stress-testing — finding gaps in trigger coverage,
instruction clarity, progressive disclosure, security, and cross-skill
coherence.
When to Load
Load this skill when any of these apply:
- Auditing an entire skills directory for quality and consistency
- Reviewing a batch of skills before publishing or distributing
- The user asks for skill quality review, audit, or improvement suggestions
- The user says "audit my skills", "skill quality check", "review these skills", "check my skills", "how good are my skills", or "validate these skills"
- Preparing a skills directory for npm packaging or distribution
- You want to identify redundant, incomplete, or poorly triggered skills
Adversarial Protocol
This skill follows the standard dual-agent adversarial pattern (Agent A: The Evaluator, Agent B: The Adversarial User). For the core pipeline rules, phase definitions, and agent switching protocols, read: ../adversarial-security/references/adversarial-base-protocol.md
For the skill audit-specific protocol with scoring and templates, read: references/multi-pass-skill-protocol.md
Audit Categories
The 8 quality categories evaluated per skill:
- Frontmatter & Triggering
- Instruction Clarity
- Structure & Progressive Disclosure
- Output Formats & Templates
- Edge Cases & Error Handling
- Security & Safety
- Token Efficiency
- Maintenance & Versioning
Additionally, 4 directory-level categories assess the collection:
- Cross-Skill Coherence
- Trigger Collision Detection
- Coverage Gap Analysis
- Ecosystem Consistency
For the full checklist, read references/audit-categories.md.
External Validation (Phase 4)
Phase 4 triggers an independent validation pass using the GitHub CLI (gh copilot).
The copilot subcommand is built into modern gh CLI — no separate extension is
needed. This provides a fundamentally different model's perspective on skill
quality, catching issues that internal review normalizes.
For prompts, read references/copilot-skill-prompts.md.
Prerequisites: gh CLI v2.x+ with gh auth status passing. If gh copilot
is not available, skip Phase 4 gracefully and note the skip in the journal entry.
Read references/copilot-usage.md for critical non-interactive execution requirements.
Feedback Loop
Every phase creates a journal entry for future retrieval. For templates and tag conventions, read references/feedback-loop.md.
Scripts
This skill includes automated helper scripts located in the scripts/ directory:
scripts/check-skills.ps1: Automated Phase 1 metric gathering (token count, trigger detection).scripts/run-copilot.ps1: Automated Phase 4 Copilot validation pipeline.
Configuration
| Variable | Default | Description |
|---|---|---|
MAX_AUDIT_PASSES | 2 | Maximum stress-test cycles (phases 2–3 repeat) |
AUDIT_DEPTH | standard | Depth: surface, standard, or thorough |
COPILOT_VALIDATION | true | Enable/disable Copilot extension validation phase |
INCLUDE_REFERENCES | true | Whether to read and evaluate reference files too |
Audit Depth Profiles
- Surface: Frontmatter + structure only (Categories 1, 3, 7). Quick scan for obvious issues. Best for large directories (30+ skills).
- Standard: All 8 per-skill categories + 4 directory-level categories. Default for most audits.
- Thorough: Full audit + extended analysis:
- Read every reference file and evaluate its quality
- Construct 3 test prompts per skill and evaluate trigger likelihood
- Analyze description keyword coverage against real user phrasing
- Compare against
skill-builder/checklist.mditem by item - Check for stale content (outdated API references, deprecated tools)
Synergies
| Skill/Workflow | Relationship |
|---|---|
skill-builder | Defines the quality standards this skill audits against |
adversarial-planner | Parent pattern — plan-level adversarial review |
adversarial-security | Sibling — audits security posture; this audits skill quality |
adversarial-performance | Sibling — audits performance; this audits skill quality |
Signals
- GitHub stars
- 20
- Forks
- 5
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
adversarial-skill-audit- Source
- github.com/neverinfamous/memory-journal-mcp