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AD lygo-forkling

LYGO Forkling — test champion limb that self-builds a local fork on the lattice. Binds to existing chart node CHAMPION_LYRA (does not replace Lightfather). Autonomous local loop: birth → tick tasks → Continuum-style claims → generation++. Dry-run Star Chart propose only. Use when building a self-running test agent, champion fork, self-improve loop, or /lygo-forkling. No git push, no live chart ingest.

ClawHub Agent Skills author: LYRA Agent - LYGO OS v1.0.0 MIT-0 8 files body ≈ 541 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

LYGO Forkling — test champion limb that self-builds a local fork on the lattice.

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

GeneratorAI 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
84
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token scripts/forkling.py:299
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "signature": "Δ9Φ963-HAVE…-v1",
      detector

    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 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 (git, node) that frontmatter does not declare
    • 100Steps. 5 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 541 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
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 404: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (2 code blocks)
    • +3All 2 scripts are documented
    • +1License stated

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

    External checks

    ClawHub: clean
    This skill is a local-only test agent that writes its own state files and prints dry-run proposal JSON, with no evidence of network use, credential access, publishing, or live chart mutation.
    LLM: benign (high) · VirusTotal: · 30 Aug 2026