SKILLEMALL.ai
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AC whoop

Fetch and analyze Whoop recovery, strain, sleep, and HRV data via the Whoop API. Use when the user asks about their Whoop metrics, recovery status, sleep quality, daily strain, HRV trends, workout data, or wants health/training insights based on Whoop data. Also use for daily morning briefings, weekly analysis, trend tracking, or real-time health alerts.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 1 794 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Fetch and analyze Whoop recovery, strain, sleep, and HRV data via the Whoop API.

As a process C 57/100 · Has gaps — weak spots: result and completion, consistency, running it twice

AnalyzerGitHubData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Consistency w 8
40
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: 9. 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 57/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (whoop) differs from the folder (whoop-openclaw-skill)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 79 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 1794 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
    • -2localhost URLs: will not work for another user
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 356: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 79 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.