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AB apple-reminders

Apple Reminders via remindctl: add, list, complete.

NousResearch/hermes-agent Hermes author: NousResearch MIT 1 file body ≈ 832 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Apple Reminders via remindctl: add, list, complete.

As a process B 66/100 · Nearly there — weak spots: running it twice, progress reporting

GeneratorAI and agentsPersonal productivitytype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
66/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Failures and branches w 10
55
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "prerequisites"

    Process rating: all ten parameters 66/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 60Consistency. The Hermes dialect needs category and tags
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 23 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 832 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)
    • +3Description length 51: 120–800 characters recommended
    • +2Single-language instructions
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 23 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +1License stated

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