Audit Integrity Skill
SkillAI & modelsAdds quality checks to your agent's security audits, forcing honest self-review and retries until findings meet a high bar.
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 Audit Integrity Skill skill
About this capability
Shared audit integrity framework for all AppSec agents — enforces output quality, intellectual honesty, and continuous improvement through anti-rationalization guards, self-critique loops, retry protocols, non-negotiable behaviors, self-reflection quality gates (1-10 scoring, ≥8 threshold), and a se
What this skill tells your AI
The instructions your AI receives, as published by github/awesome-copilot in skills/audit-integrity/SKILL.md and read by ahel’s review.
Enforces output quality, intellectual honesty, and continuous improvement across all AppSec agents.
When to Use
- Every security analysis, code review, threat model, or quality scan agent run
- Applied automatically as a post-analysis quality gate
- Applicable to any agent performing SAST, SCA, threat modeling, or code quality analysis
Components
This skill provides 7 reusable capabilities. Agents apply all 7 unless their scope excludes a specific component.
| Component | Reference File | Purpose |
|---|---|---|
| Clarification Protocol | clarification-protocol.md | Ask ≤2 targeted questions before analysis when scope is ambiguous |
| Anti-Rationalization Guard | anti-rationalization-guard.md | Table of prohibited rationalizations with mandatory responses |
| Self-Critique Loop | self-critique-loop.md | Mandatory second-pass review after initial analysis |
| Retry Protocol | retry-protocol.md | Tool failure handling — retry once, then document |
| Non-Negotiable Behaviors | non-negotiable-behaviors.md | Hard rules: never fabricate, always cite evidence, report gaps |
| Self-Reflection Quality Gate | self-reflection-quality-gate.md | 1–10 scoring rubric with ≥8 threshold per category |
| Self-Learning System | self-learning-system.md | Lesson/Memory templates and governance rules |
Execution Flow
- Before analysis: Apply Clarification Protocol if scope is ambiguous
- During analysis: Apply Anti-Rationalization Guard at every decision point
- After initial pass: Execute Self-Critique Loop (mandatory second pass)
- On tool failure: Apply Retry Protocol
- Before delivery: Run Self-Reflection Quality Gate (all categories must score ≥8)
- After delivery: Create Lessons/Memories for novel findings, false positives, or methodology gaps (see Self-Learning System)
Agent-Specific Adaptation
Each agent customizes the Self-Critique Loop checklist and Self-Reflection Quality Gate categories to match its domain. The reference files provide the base templates; agents extend them with domain-specific items.
Example extensions per agent type
- SAST/SCA agents: Add taint trace completeness and manifest coverage checks
- SonarQube-style agents: Add rating sanity check (A–E consistency with findings)
- Threat modeling agents: Add STRIDE category completeness per trust boundary
- Code review agents: Add trust boundary audit with data flow tracing
Signals
- GitHub stars
- 39k
- Forks
- 5k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
audit-integrity- Source
- github.com/github/awesome-copilot