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github/audit-integrity

github

audit-integrity

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.

globalCross-platform. Works with any language or framework analyzed by AppSec agents.
version:1.0
New~789
v1.0Saved Jun 26, 2026

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
Files8
8 files · 15.1 KB

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Overall Score

88/100

Grade

A

Excellent

Safety

90

Quality

88

Clarity

87

Completeness

85

Summary

This is a shared audit integrity framework designed to enforce quality, intellectual honesty, and continuous improvement across all AppSec security analysis agents. It provides seven reusable components (clarification protocol, anti-rationalization guard, self-critique loop, retry protocol, non-negotiable behaviors, self-reflection quality gate, and self-learning system) that agents apply during analysis to guard against rationalization, ensure evidence-based reporting, and continuously improve through lessons and memories.

Detected Capabilities

File reading (referenced files)Knowledge base management (lessons/memories directory)Self-reflection and scoring logicDocumentation templates and governance rules

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

audit integrity frameworksecurity analysis quality gateprevent findings rationalizationthreat model completenessevidence-based reportingsecurity lessons repositoryappsec agent coordination

Use Cases

  • Enforce quality gates on security analysis outputs across SAST, SCA, threat modeling, and code review agents
  • Prevent rationalization and incomplete coverage in threat models by requiring STRIDE category completeness per trust boundary
  • Catch false positives and methodology gaps through mandatory second-pass self-critique before delivering findings
  • Build institutional knowledge by capturing lessons from false positives, missed categories, and tool limitations in a searchable repository
  • Validate findings are evidence-backed (file:line, CVE ID, component reference) rather than speculative before reporting
  • Handle tool failures gracefully by documenting gaps and retrying once rather than silently skipping analysis phases

Quality Notes

  • Excellent scope clarity: explicitly documents that this is a meta-skill for AppSec agents, not a primary analysis tool
  • Strong enforcement of evidence-based reporting: requires file:line, CVE ID, or component references for every finding — prevents speculation and hallucination
  • Well-structured domain-specific extensions: provides domain-scoped checklists for STRIDE, SAST/SCA, Code Quality (SonarQube-style), and multi-tool pipelines rather than one-size-fits-all
  • Comprehensive coverage of agent failure modes: anti-rationalization guard identifies and blocks common shortcuts (e.g., 'looks fine', 'probably lower risk in practice')
  • Self-learning system with governance: lesson/memory templates and dedup/supersede rules prevent knowledge base bloat and conflicting guidance
  • Clear execution flow diagram helps agents understand ordering (clarification → anti-rationalization → critique → retry → quality gate → learning)
  • Non-negotiable behaviors are properly hardened: 'never fabricate findings', 'always cite evidence', 'complete all phases' with explicit gap reporting
  • Self-reflection quality gate uses quantified threshold (≥8 on 1-10 scale) rather than vague 'good enough' — operationalizable by agents
  • Retry protocol is pragmatic: retry once, then document rather than infinite retry loops or silent failure
  • File manifest is complete: all 7 referenced files present, no broken links
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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