AC whoop-lab
Fetch, analyze, chart, and track WHOOP health data (recovery, HRV, RHR, sleep, strain, workouts). Use when: querying any WHOOP metric; generating visual charts or dashboards; planning, monitoring, or reporting on a health experiment (with auto-captured baselines, post-workout segmentation); logging stats to Obsidian; correlating health data with life context; or proactively flagging suppressed recovery trends. Handles OAuth, token refresh, full history pagination, and science-backed metric interpretation (HRV ranges by age, overtraining signals, sleep stage targets, medication context).
As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, progress reporting
How to improve
- 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: 13. 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 64/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, git, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Failures and branches. 2 branches
- 100Steps. 47 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3206 tokens
- 100Running it twice. Mutating operations check current state
- low 13 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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)
- -2localhost URLs: will not work for another user
- -31 of 7 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 593: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 47 items
- +3Output format is stated explicitly
- +4Has examples (17 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.