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AD lygo-quantum-attestor

LYGO Quantum Attestor — hooks Protocol 6 attestation to Biophase7 anchors and SLM Merkle gossip. Verifies node integrity with Δ9 seals. Emits non-collapsing receipts. Local-first, consent-gated. Pairs with continuum-integrator + geodesic-sealer. No network, no subprocess, no auto-publish. Hooks: attest / verify-node / emit-receipt / seal-delta9.

ClawHub Agent Skills author: LYRA Agent - LYGO OS v1.0.1 MIT-0 9 files body ≈ 862 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

LYGO Quantum Attestor — hooks Protocol 6 attestation to Biophase7 anchors and SLM Merkle gossip.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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%
94
Quality 40%
92
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • low Secrets in code secret-high-entropy-token claw.json:11
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "signature": "Delt…TOR",
      detector
    • low Secrets in code secret-high-entropy-token references/SKILLSPECTOR_AUDIT.md:3
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)
      **Signature:** `Delt…TOR`
      detectorfixture
    • low Secrets in code secret-high-entropy-token scripts/attestor_cli.py:10
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; test fixture / example file)
      Blueprint: @grok · Signature: Delt…TOR
      detectorfixture
    • low Secrets in code secret-high-entropy-token scripts/attestor_cli.py:23
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
      SIG = "Delt…TOR"
      fixturequoted
    • low Secrets in code secret-high-entropy-token SKILL.md:15
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      signature: "Delt…TOR"
      detector
    • low Secrets in code secret-high-entropy-token SKILL.md:37
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      **Signature:** `Delt…TOR`
      detector

    Files scanned: 9. 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 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 862 tokens
    • 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
    • +2Single-language instructions
    • +3Description length 347: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This skill is a local attestation utility whose file reads and consent-gated writes match its stated purpose, with no evidence of network use, subprocesses, persistence, or hidden behavior.
    LLM: benign (high) · VirusTotal: · 22 Aug 2026