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BD apple-health-analysis

Apple Health 数据全景分析。从 export.zip 流式解析 XML（支持 1-2GB 大文件），提取 RHR/HRV/VO₂Max/睡眠/步数/血氧等核心指标，基于用户个人信息（年龄/性别/身高/体重/病史）动态校准参考范围，生成个性化交互式 HTML 报告（含 6 张 Chart.js 图表）。使用场景：用户说「帮我分析健康数据」「看看我的 Apple Health 数据」「生成健康报告」「分析运动/睡眠/心率趋势」时使用。

ClawHub Agent Skills author: Gloriaameng v1.0.0 MIT-0 4 files body ≈ 1 055 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype 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
B
89/100
safety, quality, tests
Safety 60%
98
Quality 40%
75
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token scripts/parse_health.py:30
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    'HKQu…Max',
    quoted
  • low Secrets in code secret-high-entropy-token scripts/parse_health.py:154
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    vo2   = monthly_mean(raw.get('HKQu…Max', []))
    quoted

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (apple-health-analysis) differs from the folder (iwatch-health-data-analysis)
  • 100Tools and files. No external tools needed
  • 100Steps. 26 steps
  • 100Execution cost. Instruction body is 1055 tokens
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 223: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (5 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This Apple Health report skill has a coherent purpose, but it handles sensitive health data with under-disclosed browser/network and local-file risks that users should review first.
LLM: suspicious (high) · VirusTotal: · 29 May 2026