Health Copilot: Eating, Sleep, and Exercise Tracking
Use this skill as the central hub for personal health tracking, analysis, and Feishu reporting.
Overview
This skill is for practical personal-health management, not medical diagnosis.
It provides a unified layer for:
- Recognition: Analyzing food photos, sleep screenshots, and workout summaries.
- Persistence: Mapping and writing results into a structured Feishu Bitable.
- Reporting: Rebuilding daily/weekly summary records and monthly dashboards.
Role & Workflow
- Classify: Identify if the request is about nutrition, sleep, exercise, or cross-domain reporting.
- Analyze: Perform domain-specific extraction or analysis (conservative and evidence-based).
- Persist: Map the results into the target Feishu schema (default English-only).
- Automate: Run maintenance scripts for monthly/weekly summaries when requested.
Routing branches
1. Nutrition
- Trigger:
log calories, nutrition analysis, meal photos, bento photos.
- Reference:
references/nutrition.md.
- Behavior: Analyze photo -> Estimate nutrients -> Upsert to Nutrition Meals table.
2. Exercise
- Trigger:
log workout, exercise analysis, app screenshots (Garmin, Strava, Apple Health).
- Reference:
references/exercise.md.
- Behavior: Analyze screenshot -> Extract metrics -> Assess load -> Upsert to Workouts table.
3. Sleep
- Trigger:
log sleep, sleep analysis, recovery status, app screenshots (AutoSleep, Oura, etc.).
- Reference:
references/sleep.md.
- Behavior: Analyze screenshot -> Extract stages/HRV -> Upsert to Sleep Recovery table.
4. Cross-domain / Reporting
- Trigger:
weekly health review, rebuild calendar, refresh dashboard, monthly summary.
- Reference:
references/cross-domain.md.
- Behavior: Rebuild Monthly Health Calendar -> Rebuild Weekly Health Assessment -> Refresh Dashboard.
Universal rules
- Internal Schema: Always target the English-only internal schema for persistence to ensure script reliability.
- Config-Driven: Use
references/config-template.md and scripts/config_loader.py for all environment variables.
- Conservative Analysis: Extract only what is visible; do not fake precision.
- Separation: Keep analysis facts separate from interpretive suggestions.
Output rule
Default response shape:
- Conclusion (what was analyzed/updated)
- Evidence (the data points)
- Uncertainty (missing info)
- Next step (action or report link)
Script mapping
scripts/bootstrap_health_tables.js: Create tables/fields.
scripts/rebuild_monthly_calendar.py: Aggregate daily rows.
scripts/rebuild_weekly_assessment.py: Generate weekly reports.
scripts/build_monthly_dashboard.js: Create/refresh dashboards.
Installation
clawdhub install personal-health-router