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AC asin-governance

Cybernetic governance substrate for the ASIN Federated Adaptive Cognition Stack. Constraint engine, safety oracle, drift detection, and audit trails for agent ecosystems. **When to use:** Any autonomous action needs pre-flight safety check, entropy accounting, or rollback capability. Part of the federated stack — not standalone.

ClawHub Agent Skills author: dbottrader v1.0.1 MIT-0 14 files · 2 scripts body ≈ 3 425 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructureAI and agentsFinancetype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
Run on models
none yet
Process rating
C
50/100
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:264
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **No credential harvesting:** HMAC secrets stored in `vault/{node_id}.secret` (owner-read-only, `0o600`)

    Files scanned: 14. 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 50/100

    • 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. 6 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 46 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3425 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 330: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    The skill is not clearly harmful, but it needs review because it handles credentials, tokens, network access, and local audit data while several advertised safety controls are weaker than described.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026