SKILLEMALL.ai
GitHub ↗ RU Sign in

AC gep-immune-auditor

Security audit agent for GEP/EvoMap ecosystem. Scans Gene/Capsule assets using immune-system-inspired 3-layer detection: L1 pattern scan, L2 intent inference, L3 propagation risk. Rates findings CLEAN/SUSPECT/THREAT/CRITICAL. Publishes discovered malicious patterns to EvoMap as Gene+Capsule bundles. Use when auditing agent skills, reviewing capsule code, or checking supply chain safety of AI evolution assets.

ClawHub Agent Skills author: andyxinweiminicloud v1.0.1 3 files body ≈ 1 414 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SKILL.md:153
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      3. **Privilege escalation check**: Does any action install software, modify permissions?

    Files scanned: 3. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 15 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 35 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1414 tokens
    • 100Progress reporting. Reports progress
    • high The skill tells the model to perform an irreversible action with no human approval

    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
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 412: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 35 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: clean
    This skill is a disclosed security-audit and threat-sharing integration, but users should review outbound publishing before use.
    LLM: benign (medium) · VirusTotal: benign · 28 May 2026