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AA appliance-service-visit-prep-sheet

Prepare a technician-ready appliance service visit sheet with model and serial details, symptom timeline, photo checklist, access notes, questions, and visit-day prep.

ClawHub Agent Skills author: haidong v1.0.1 MIT-0 3 files body ≈ 2 009 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process A 83/100 · Runs to the end — weak spots: running it twice

ProcedureAI and agentstype 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%
89
Run on models
none yet
Process rating
A
83/100
Runs to the end
Running it twice w 4
30
Result and completion w 14
60
Inputs and preconditions w 11
70
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "displayName"
    • note frontmatter-key unknown frontmatter key "triggerKeywords"
    • note frontmatter-key unknown frontmatter key "language"
    • note frontmatter-key unknown frontmatter key "hasExecutableCode"
    • note frontmatter-key unknown frontmatter key "promptOnly"
    • note frontmatter-key unknown frontmatter key "execution"

    Process rating: all ten parameters 83/100

    • 30Running it twice. 2 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 4 branches
    • 85Steps. 82 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2009 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 10 top-level sections: this looks like several domains in one skill

    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
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 167: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 82 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a prompt-only checklist skill for preparing an appliance repair visit and the provided artifacts do not show suspicious behavior.
    LLM: benign (medium) · VirusTotal: benign · 12 May 2026