SKILLEMALL.ai

AB hercycle

Women's cycle intelligence companion. Reads Whoop biometric data (HRV, recovery, sleep, skin temperature) and menstrual cycle phase to understand which hormonal "season" the user is in — and takes smart, phase-aware actions. Use when the user asks about their cycle phase, energy, mood, what to eat, how hard to train, when to schedule important meetings, or wants any Whoop-powered recommendation tailored to their hormonal cycle. NOT a period tracker — a biometric intelligence layer that treats the cycle as a source of power, not a problem.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 988 tokens Open the sourcegithub.com analyzed 2 d ago

Women's cycle intelligence companion.

As a process B 66/100 · Nearly there — weak spots: result and completion, failures and branches, running it twice

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Failures and branches w 10
0
Running it twice w 4
30
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 66/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 2 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 988 tokens
    • 100Progress reporting. Reports progress

    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
    • +4No input/output examples
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 544: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 15 items
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.