Apple Health Query Skill

Use this skill to analyze Apple Health data already synced into local SQLite by the collector.

Tools to use

• Shell tool: run scripts/query_health.py to pull fresh JSON data.
• HTTP tool: optionally verify collector availability with /healthz before troubleshooting missing data.

Workflow

1. (Optional health check) Verify collector is reachable when user reports stale/missing data:
   • curl -fsS http://\x3Ccollector-host>:8443/healthz
   • if TLS is enabled: curl -fsS https://\x3Ccollector-host>:8443/healthz
2. Query the local database via shell (preferred default):
   • python scripts/query_health.py --window-hours 24 --sleep-nights 7 --types heart_rate,glucose,sleep_stage
3. Adjust parameters to user intent:
   • Short check-in: --window-hours 1
   • Daily review: --window-hours 24 --sleep-nights 7
   • Alert follow-up: keep --types narrow to requested metrics.
4. Summarize with numbers and timestamps, then give practical next steps.
5. Always include safety framing: informational only, not medical advice; suggest clinician contact for extreme/persistent readings.

Output expectations

Base your answer on the returned JSON fields:

• generated_at, user_id, window_hours
• quantity.\x3Cmetric>[] entries with {ts, value, unit, source, device}
• sleep[] entries with {start_ts, end_ts, category, source, device}

Response style guardrails

• Cite the exact time window used (for example: “last 24 hours” and “last 7 nights”).
• Highlight notable thresholds carefully (e.g., glucose outside typical 70–180 mg/dL range).
• Tie suggestions to observed values; avoid generic advice.
• Never hardcode user IDs or DB paths—scripts/query_health.py already reads .env.

Live Heart Rate

The collector stores real-time BLE heart rate events from Wahoo HR straps (and compatible sensors) in the live_events table. Use this for workout monitoring, recent-activity check-ins, or comparing live readings against resting baseline.

Tools to use

• Shell tool: run scripts/query_live_hr.py to pull live HR events and per-session summaries.

Workflow

1. Query recent live HR events (default: last 60 minutes):
   • python scripts/query_live_hr.py --window-minutes 60
2. Narrow to the last workout or a specific timeframe:
   • python scripts/query_live_hr.py --window-minutes 120
3. Filter to a specific session (use a session UUID from a previous query):
   • python scripts/query_live_hr.py --session-id \x3Cuuid> --window-minutes 180
4. Filter to a specific device (useful when multiple sensors are paired):
   • python scripts/query_live_hr.py --device-id \x3Cdevice_id> --window-minutes 60

Output fields

Base your answer on the returned JSON:

• summary — aggregate stats for the queried window:
  • count — total event count
  • latest_bpm, min_bpm, max_bpm, avg_bpm, median_bpm, stddev_bpm
  • latest_zone — heart rate zone: resting (\x3C60), normal (60–99), elevated (100–139), high (≥140)
• sessions[] — per-session breakdown with session_id, event_count, start_ts, end_ts, device_name, avg_bpm, min_bpm, max_bpm
• events[] — individual readings with ts, value (BPM), session_id, seq, device_id, source_device_name

Guidance

• Workout summary: report avg_bpm, max_bpm, and duration (start_ts → end_ts of the session).
• Zone coaching: map latest_zone to practical advice (e.g., elevated zone during recovery warrants attention).
• Unusual readings: flag sustained HR >150 or \x3C40 bpm; recommend clinician contact if persistent.
• Data freshness: if count is 0, the sensor may not be paired or the session may not have started — advise the user to open the Live HR screen on their iPhone.