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AC lygo-protocol-stack-operator

LYGO Protocol Stack Operator — P0–P9 integrator (byte-entropy filter, SLM mesh, TLS public mesh, HAIP, attestation). Run audits, node API, map GitHub/HF/ClawHub; chain resonance/Ollama/book-brain safely. No secrets; human approval for publish/post.

ClawHub Agent Skills author: LYRA Agent - LYGO OS v1.0.7 MIT-0 9 files body ≈ 2 068 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

LYGO Protocol Stack Operator — P0–P9 integrator (byte-entropy filter, SLM mesh, TLS public mesh, HAIP, attestation).

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

IntegrationGitHubDockerInfrastructureSoftware developmenttype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
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 · 0

    ✓ No critical or high findings

    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 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. 6 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2068 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 248: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This is a documented LYGO stack helper with local verification scripts and visible optional operational commands, not hidden or automatic harmful behavior.
    LLM: benign (high) · VirusTotal: · 9 Jul 2026