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name audit-integrity
description 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 self-learning system with lesson/memory governance for security analysis agents.
compatibility Cross-platform. Works with any language or framework analyzed by AppSec agents.
metadata
version
1.0

Audit Integrity Skill

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

  1. Before analysis: Apply Clarification Protocol if scope is ambiguous
  2. During analysis: Apply Anti-Rationalization Guard at every decision point
  3. After initial pass: Execute Self-Critique Loop (mandatory second pass)
  4. On tool failure: Apply Retry Protocol
  5. Before delivery: Run Self-Reflection Quality Gate (all categories must score ≥8)
  6. 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