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AC 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.

github/awesome-copilot Agent Skills author: github MIT 8 files body ≈ 695 tokens Open the sourcegithub.com↗ analyzed 14 h ago

Shared audit integrity framework for all AppSec agents — enforces output quality, intellectual honesty, and continuous improvement through…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
96
Quality 40%
88
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Risky intent intent-offensive-security references/anti-rationalization-guard.md:37
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (documentation table row)
      | "Lateral movement is unlikely here"            | Document the specific architectural control that prevents pivoting and verify it is implemented — do not assume. |
      table
    • low Risky intent intent-offensive-security references/self-critique-loop.md:20
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ### STRIDE-LM (Lateral Movement)
    • low Risky intent intent-offensive-security references/self-critique-loop.md:24
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      6. **Lateral movement audit**: Re-trace all identified pivot paths. Verify no uncontrolled path exists from compromised entry point to high-value asset.
    • low Risky intent intent-offensive-security references/self-reflection-quality-gate.md:42
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **Coverage** | Were all lateral movement paths, trust boundaries, and post-exploitation chains assessed? | ≥ 8 |

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 695 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 378: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.