SKILLEMALL.ai

AC appointment-manager

Books, tracks, and reminds you about every appointment. Use when a user wants to book, log, or be reminded about any appointment.

ClawHub Agent Skills author: Nico Lumma v1.0.0 MIT-0 2 files body ≈ 2 627 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
57/100
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Implicit map keys need to be followed by map values at line 19, column 1: Every appointment has three problems: booking it, remembering it, and following… This skill handles all three. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    Process rating: all ten parameters 57/100

    • 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
    • 70Failures and branches. 4 branches
    • 85Steps. 38 steps, 3 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2627 tokens
    • 100Progress reporting. Reports progress
    • low 14 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
    • +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 129: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (16 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

    External checks

    ClawHub: suspicious
    This appointment skill is coherent, but it asks for broad authority to book real appointments, store sensitive details, update calendars, and send reminders beyond what it clearly declares.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026