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Apple Watch health data sync via Health Auto Export app. Use when querying sleep, heart rate, steps, workouts, or any health metrics. Also use when setting up or troubleshooting the health data pipeline.

Not recommendedcritical or high security findings · low grade F
ClawHub Agent Skills author: LainNet-42 v1.0.0 3 files body ≈ 2 910 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, when it triggers

ProcedureInfrastructureData 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
F
38/100
safety, quality, tests
Safety 60%
7
Quality 40%
85
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 8

  • high Dangerous commands cmd-persistence SKILL.md:41
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    Register-ScheduledTask -TaskName "HealthSyncServer" -Action $action -Trigger $trigger -RunLevel Highest -Force
  • high Dangerous commands cmd-persistence SKILL.md:47
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    cat > ~/Library/LaunchAgents/com.health-sync.server.plist << 'EOF'
  • high Dangerous commands cmd-persistence SKILL.md:65
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl load ~/Library/LaunchAgents/com.health-sync.server.plist
  • high Dangerous commands cmd-persistence SKILL.md:219
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    Register-ScheduledTask -TaskName "HealthSyncServer" -Action $action -Trigger $trigger -RunLevel Highest -Force
  • high Dangerous commands cmd-persistence SKILL.md:224
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    # create plist at ~/Library/LaunchAgents/com.health-sync.server.plist
Medium and low: 3
  • low Dangerous commands cmd-background-process SKILL.md:73
    Starts a background / autostarted process
    nohup python3 server.py > server.log 2>&1 &
  • low Dangerous commands cmd-background-process SKILL.md:234
    Starts a background / autostarted process
    nohup python3 server.py > server.log 2>&1 &
  • low Exfiltration read-dotenv SKILL.md:270
    Reads a .env file (quoted — discussed, not commanded)
    python -c "import json; print(json.load(open('.env.json'))['api_key'])"
    quoted

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 69/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 100Steps. 69 steps
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 6 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2910 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 16 top-level sections: this looks like several domains in one skill

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 203: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 69 items
  • +4Has examples (16 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

External checks

ClawHub: suspicious
This skill appears intended for local Apple Watch health syncing, but it handles sensitive health data with overly broad persistence, privilege, sharing, and network exposure defaults.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026