SKILLEMALL.ai

AC ratgdo32-disco

Control a ratgdo32 disco garage door opener via its local web API. Use when the user asks to open/close the garage, check garage status, toggle the garage light, check if a car is parked, enable/disable remotes, or anything involving the garage door. Supports door control, light, obstruction detection, vehicle presence (laser sensor), parking assist, motion, and remote lockout. Uses local network trust model (LAN-only, no internet exposure).

ClawHub Agent Skills author: Michael Boland v1.0.1 MIT-0 3 files · 1 script body ≈ 1 216 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. 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 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1216 tokens
    • 100Running it twice. No mutating operations
    • 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
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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 445: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (4 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This skill openly controls a local garage door device, but its broad trigger wording and safety-check gaps make accidental physical actions a real review concern.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026