weekly-health

Solid

Weekly health review — sleep trends, workout adherence across wger/Fitbod/Peloton, nutrition averages, and recovery analysis.

Code & Development 11 stars 6 forks Updated 1 months ago MIT

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Quality Score: 74/100

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Issue Health 10%
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License 10%
100
Description 5%
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Skill Content

# /weekly-health — Weekly Health Review Comprehensive 7-day health review aggregating data from all health sources. ## Steps ### 1. Sleep Trends (Oura — 7 days) - Average sleep score, duration, efficiency - HRV trend (improving, declining, stable) - Resting HR trend - Best and worst nights - Bedtime consistency (std deviation of bedtime) ### 2. Activity Summary (Apple Health — 7 days) SQL query for the past 7 days: - Average daily steps vs target - Total active energy burned - Exercise minutes per day - Days meeting activity goals ### 3. Workout Adherence (wger + direct Fitbod/Peloton — 7 days) - Pull `wger` first: - `get_workout_sessions` - `get_workout_log` - `get_routines` - If `wger` is empty or incomplete, use direct Fitbod detail from the latest export for weight training only: - `fitbod_parse_csv` for strength-session count, last lifting date, exercise list, sets/reps/load, and deduped strength rows - `fitbod_preview_mapping` or `fitbod_list_exercise_aliases` only when you need exercise-to-category confidence for muscle-group or progression claims - `fitbod_import_csv` with `dry_run=true` is allowed when you need date-bounded weekly rows, coverage verification, or an unresolved-exercise queue without persisting anything - Only treat muscle-group rollups as reliable when trailing-365d weighted mapping coverage is at least 90% - If coverage is below 90%, report exercise-level detail and explicitly label muscle-group rollups as partial - Never pe...

Details

Author
hvkshetry
Repository
hvkshetry/StewardOS
Created
9 months ago
Last Updated
1 months ago
Language
Python
License
MIT

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