Apple Health Analyzer
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About this skill
Problem
Apple Health exports can be 100MB to 2GB or larger, which is impractical to read into an agent context. The same metric may also come from iPhone, Apple Watch, or third-party apps, creating duplicates, Unicode whitespace variants, and timezone handling issues. The core need is a reproducible parsing pipeline rather than raw file inspection.
How It Works
The skill first runs --profile-only to generate a compact JSON summary and confirm available data types. It then streams the XML with iterparse and calls elem.clear() to release memory. High-frequency series such as HeartRate and StepCount are aggregated to daily statistics, while low-frequency values such as HRV and VO2Max are preserved as daily records. Multi-source data is deduplicated with an Apple Watch > iPhone priority, and text fields are normalized with NFKC.
The analysis stage generates Chinese-localized interactive dashboards with Plotly, covering activity, workouts, heart rate, sleep, menstrual cycle, body composition, and swimming. v2.2.0 produces three standalone HTML reports: comprehensive health analysis, sleep deep-dive, and yearly overview. It also uses personal P25-P75 baselines for recovery assessment, stress flags, and sleep-to-HRV correlation analysis.
Boundaries
It assumes a health_data/ working directory and Python scripts, making it suited to local analysis of Apple Health exports. Timezone handling is currently simplified, so travelers crossing time zones may see slight date attribution drift. Data types with insufficient coverage are skipped. The outputs are statistical visualizations and decision-support insights, not medical diagnosis.
Use Cases
- After receiving an Apple Health XML export, profile the data first and generate a localized interactive health overview.
- Break multi-year sleep, heart rate, and HRV into trend charts and explore deep-sleep share versus next-day recovery.
- Extract SWOLF, stroke mix, and water-temperature relationships from swim workouts to build a personal progress report.
- Before a yearly review, inspect the yearly overview heatmap for data types by year and device source distribution.
Best For
- Data engineers maintaining local health-data scripts who need shareable HTML reports from Apple Health XML.
- Quantified-health users tracking sleep and workouts who need to compare deep sleep, HRV, and resting heart-rate recovery.
- Swim athletes who need SWOLF, stroke distance, and water-temperature effects extracted from workout records.
- Health-analytics researchers who need personal P25-P75 baselines and stress flags to screen anomalous days.
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