SKILLEMALL.ai
GitHub ↗ RU Sign in

AC whoop

Sync WHOOP health data (recovery, sleep, strain, workouts) to markdown files for AI-powered health insights. Use when user asks about WHOOP data, health metrics, recovery scores, sleep analysis, HRV, strain tracking, or wants daily health reports. Triggers on "WHOOP", "recovery score", "HRV", "sleep debt", "strain", "health sync", "健康数据", "恢复分数", "睡眠", "心率变异性".

ClawHub Agent Skills author: aikong-cmd v1.0.0 MIT-0 7 files body ≈ 598 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerData and analyticsInfrastructuretype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 7. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (whoop) differs from the folder (whoop-health-sync)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 6 steps
    • 100Execution cost. Instruction body is 598 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

    • +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
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 363: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 6 items
    • +4Has examples (5 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    The skill’s health-sync purpose is mostly coherent, but it ships a populated WHOOP OAuth token file and has weak handling around long-lived health-data access.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026