health-data — Claude Code skill: what it does, how to install
Queries a local Apple Health SQLite database for vitals, sleep, activity, and workouts with output in Markdown, JSON, ASCII, or FHIR R4.
npx skills add glebis/claude-skills --skill health-data -a claude-codeWhat health-data does
This skill connects your coding agent to a local SQLite database at ~/data/health.db. The database contains 6.3M+ records across 43 metrics, from heart rate and sleep stages to mobility and workout logs. The agent accesses data through scripts/health_query.py or by executing direct SQL queries via sqlite3.
Output formats include Markdown tables, structured JSON, terminal ASCII charts, and FHIR R4 bundles mapped with LOINC codes. Pre-built commands let the agent inspect daily summaries, compute week-over-week trends, analyze deep sleep hours, and check activity rings.
The skill will fail immediately if ~/data/health.db does not exist. You must export your Apple Health data and import it into SQLite beforehand.
What it helps with
- query daily summaries for steps, calories, and heart rate
- inspect sleep stages across deep and REM sessions
- export Apple Health metrics into FHIR R4 Bundles
- review workout history filtered by exercise type
Example prompts for the agent
- Show my daily health summary for 2025-11-29 in markdown format
- Analyze my sleep stages over the past 14 days using scripts/health_query.py
- Run a query on ~/data/health.db to compare resting heart rate weekly trends
How to install health-data
1Skills CLI (Vercel)
npx skills add glebis/claude-skills --skill health-data -a claude-code2Manually: put the files in place
~/.claude/skills/health-data/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/health-data/
1Skills CLI (Vercel)
npx skills add glebis/claude-skills --skill health-data -a codex2Manually: put the files in place
.agents/skills/health-data/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add glebis/claude-skills --skill health-data -a cursor2Manually: put the files in place
.cursor/skills/health-data/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install glebis/claude-skills health-data2Manually: put the files in place
.github/skills/health-data/SKILL.md1Skills CLI (Vercel)
npx skills add glebis/claude-skills --skill health-data -a gemini-cli2Platform CLI
gemini skills install https://github.com/glebis/claude-skills --path health-dataManually: put the files in place
.devin/skills/health-data/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add glebis/claude-skills --skill health-data -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about health-data
How to install health-data in Claude Code?
Run npx skills add glebis/claude-skills --skill health-data -a claude-code in your terminal.
What does the health-data skill do?
It queries Apple Health records from a local SQLite database and formats health metrics into Markdown, JSON, ASCII, or FHIR R4.
Where does health-data look for the database?
The scripts look for the SQLite database at ~/data/health.db. If that file is missing, queries cannot run.
Source text
SKILL.md · MIT
---
name: health-data
description: Query Apple Health SQLite database for vitals, activity, sleep, and workouts. Supports Markdown, JSON, and FHIR R4 output formats. This skill should be used when analyzing health metrics, generating health reports, answering questions about fitness or sleep patterns, or exporting health data in standard formats.
---
# Apple Health Data Query Skill
Query and analyze health data from the local SQLite database containing 6.3M+ records across 43 health metrics.
## Database Location
```
~/data/health.db
```
## Query Methods
### 1. Python Script (Recommended for Common Queries)
Use `scripts/health_query.py` for pre-built queries with automatic formatting:
```bash
# Daily summary
python ~/.claude/skills/health-data/scripts/health_query.py --format markdown daily --date 2025-11-29
# Weekly trends
python ~/.claude/skills/health-data/scripts/health_query.py --format json weekly --weeks 4
# Sleep analysis
python ~/.claude/skills/health-data/scripts/health_query.py --format fhir sleep --days 7
# Latest vitals
python ~/.claude/skills/health-data/scripts/health_query.py vitals
# Activity rings
python ~/.claude/skills/health-data/scripts/health_query.py --format json activity --days 30
# Workout history
python ~/.claude/skills/health-data/scripts/health_query.py workouts --days 30 --type Running
# Custom SQL
python ~/.claude/skills/health-data/scripts/health_query.py --format json query "SELECT * FROM workouts LIMIT 5"
```
**Output formats:** `markdown`, `json`, `fhir`, `ascii`
### 2. Direct SQL (For Custom/Ad-hoc Queries)
For flexible queries, run SQL directly against the database. See `references/schema.md` for table structures and query templates.
```bash
sqlite3 ~/data/health.db "SELECT AVG(value) FROM health_records WHERE record_type LIKE '%HeartRate%' AND start_date LIKE '2025-11%'"
```
## Pre-built Queries
### Daily Health Summary
Get today's key metrics:
```bash
python ~/.claude/skills/health-data/scripts/health_query.py daily
```
Returns: steps, calories, heart rate (avg/min/max), exercise minutes, distance, activity ring status.
### Weekly Trends
Compare week-over-week performance:
```bash
python ~/.claude/skills/health-data/scripts/health_query.py weekly --weeks 4
```
Returns: average daily steps, resting HR, exercise minutes, workout count per week.
### Sleep Analysis
Analyze sleep patterns:
```bash
python ~/.claude/skills/health-data/scripts/health_query.py sleep --days 14
```
Returns: nightly duration, sleep stages (Core, Deep, REM), average sleep hours.
### Latest Vitals
Get most recent vital readings:
```bash
python ~/.claude/skills/health-data/scripts/health_query.py vitals
```
Returns: Heart Rate, HRV, Resting HR, Blood Oxygen, Respiratory Rate with timestamps.
### Activity Rings
Track ring completion:
```bash
python ~/.claude/skills/health-data/scripts/health_query.py activity --days 30
```
Returns: daily ring values/goals, completion percentages, perfect day count.
### Workout History
Review exercise sessions:
```bash
python ~/.claude/skills/health-data/scripts/health_query.py workouts --days 30 --type Running
```
Returns: workout type, duration, distance, calories, summary by type.
## Output Formats
### Markdown (default)
Human-readable tables and lists. Best for reports and summaries.
### JSON
Structured data for programmatic use:
```json
{
"date": "2025-11-29",
"metrics": {
"steps": 8542,
"active_calories": 450.5,
"heart_rate": {"avg": 72.3, "min": 52, "max": 145}
}
}
```
### FHIR R4
Healthcare interoperability format. Outputs as FHIR Bundle with Observation resources using LOINC codes. See `references/fhir_mappings.md` for code mappings.
### ASCII
Terminal-friendly output with bar charts and statistics:
```
============================================================
DAILY SUMMARY - 2025-11-29
============================================================
METRICS
----------------------------------------
steps 2620
active_calories 234.5
heart_rate avg: 67.5 min: 52 max: 108
ACTIVITY RINGS
----------------------------------------
move [███████░░░░░░░░░░░░░] 36.7% (238/650)
exercise [░░░░░░░░░░░░░░░░░░░░] 0.0% (0/35)
stand [████████████████████] 100.0% (10/10)
```
## Common SQL Patterns
For ad-hoc queries, use these patterns from `references/schema.md`:
**Heart rate by hour (circadian pattern):**
```sql
SELECT strftime('%H', start_date) as hour, ROUND(AVG(value), 1) as avg_hr
FROM health_records
WHERE record_type = 'HKQuantityTypeIdentifierHeartRate'
AND value BETWEEN 40 AND 200
GROUP BY hour ORDER BY hour;
```
**Steps per day this month:**
```sql
SELECT DATE(start_date) as day, SUM(value) as steps
FROM health_records
WHERE record_type = 'HKQuantityTypeIdentifierStepCount'
AND start_date >= DATE('now', 'start of month')
GROUP BY day ORDER BY day;
```
**Sleep quality (deep + REM hours):**
```sql
SELECT DATE(start_date) as night,
ROUND(SUM(duration_minutes)/60.0, 1) as quality_hours
FROM sleep_sessions
WHERE sleep_stage IN ('Deep', 'REM')
GROUP BY night ORDER BY night DESC LIMIT 14;
```
**Workout summary:**
```sql
SELECT REPLACE(workout_type, 'HKWorkoutActivityType', '') as type,
COUNT(*) as count, ROUND(SUM(duration_minutes)) as total_min
FROM workouts
WHERE start_date >= DATE('now', '-30 days')
GROUP BY type ORDER BY count DESC;
```
## Record Types Available
The database contains 43 health metric types including:
**Vitals:** Heart Rate, HRV, Resting HR, Blood Oxygen, Respiratory Rate, Blood Pressure
**Activity:** Steps, Distance, Active Calories, Basal Calories, Flights Climbed, Exercise Time, Stand Time
**Mobility:** Walking Speed, Step Length, Walking Asymmetry, Stair Speed, Walking Steadiness
**Body:** Weight, BMI, Body Fat %
**Audio:** Environmental Noise, Headphone Exposure
**Other:** VO2 Max, Time in Daylight, UV Exposure
## Data Coverage
- **Records:** 6.3M+ measurements
- **Date range:** 2015-10-13 to present
- **Workouts:** 1,435 sessions
- **Sleep sessions:** 40,514 records
- **Activity days:** 1,875 daily summaries
## Resources
### scripts/
- `health_query.py` - Main query tool with Markdown/JSON/FHIR output
### references/
- `schema.md` - Database schema, record type mappings, SQL query templates
- `fhir_mappings.md` - LOINC codes and FHIR R4 templates
## Troubleshooting
**Database not found:**
Ensure `~/data/health.db` exists. Run the import script from `/Users/server/apple_health_export/`:
```bash
python import_health.py --status
```
**No data for date range:**
Check available date range:
```sql
SELECT MIN(start_date), MAX(start_date) FROM health_records;
```
**Outlier values:**
Filter physiologically valid ranges (e.g., heart rate 40-200 bpm):
```sql
WHERE value BETWEEN 40 AND 200
```
© glebis · MIT · glebis/claude-skills