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- OpenClaw
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Garmer
Extract health and fitness data from Garmin Connect including activities, sleep, heart rate, stress, steps, and body composition. Use when the user asks about their Garmin data, fitness metrics, sleep analysis, or health insights.
skill openclawclawhub Free
Extract health and fitness data from Garmin Connect including activities, sleep, heart rate, stress, steps, and body composition. Use when the user asks about their Garmin data, fitness metrics, sleep analysis, or health insights.
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- README.md, SKILL.md, examples/basic_usage.py, examples/moltbot_integration.py, pyproject.toml, references/REFERENCE.md
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- 1.0.2
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Documentation
Garmer - Garmin Data Extraction Skill
This skill enables extraction of health and fitness data from Garmin Connect for analysis and insights.
Prerequisites
A Garmin Connect account with health data The garmer CLI tool installed (see installation options in metadata)
Authentication (One-Time Setup)
Before using garmer, authenticate with Garmin Connect: garmer login This will prompt for your Garmin Connect email and password. Tokens are saved to ~/.garmer/garmin_tokens for future use. To check authentication status: garmer status
Daily Summary
Get today's health summary (steps, calories, heart rate, stress): garmer summary # For a specific date: garmer summary --date 2025-01-15 # Include last night's sleep data: garmer summary --with-sleep garmer summary -s # JSON output for programmatic use: garmer summary --json # Combine flags: garmer summary --date 2025-01-15 --with-sleep --json
Sleep Data
Get sleep analysis (duration, phases, score, HRV): garmer sleep # For a specific date: garmer sleep --date 2025-01-15
Activities
List recent fitness activities: garmer activities # Limit number of results: garmer activities --limit 5 # Filter by specific date: garmer activities --date 2025-01-15 # JSON output for programmatic use: garmer activities --json
Activity Detail
Get detailed information for a single activity: # Latest activity: garmer activity # Specific activity by ID: garmer activity 12345678 # Include lap data: garmer activity --laps # Include heart rate zone data: garmer activity --zones # JSON output: garmer activity --json # Combine flags: garmer activity 12345678 --laps --zones --json
Health Snapshot
Get comprehensive health data for a day: garmer snapshot # For a specific date: garmer snapshot --date 2025-01-15 # As JSON for programmatic use: garmer snapshot --json
Export Data
Export multiple days of data to JSON: # Last 7 days (default) garmer export # Custom date range garmer export --start-date 2025-01-01 --end-date 2025-01-31 --output my_data.json # Last N days garmer export --days 14
Utility Commands
# Update garmer to latest version (git pull): garmer update # Show version information: garmer version
Python API Usage
For more complex data processing, use the Python API: from garmer import GarminClient from datetime import date, timedelta # Use saved tokens client = GarminClient.from_saved_tokens() # Or login with credentials client = GarminClient.from_credentials(email="user@example.com", password="pass")
User Profile
# Get user profile profile = client.get_user_profile() print(f"User: {profile.display_name}") # Get registered devices devices = client.get_user_devices()
Daily Summary
# Get daily summary (defaults to today) summary = client.get_daily_summary() print(f"Steps: {summary.total_steps}") # Get for specific date summary = client.get_daily_summary(date(2025, 1, 15)) # Get weekly summary weekly = client.get_weekly_summary()
Sleep Data
# Get sleep data (defaults to today) sleep = client.get_sleep() print(f"Sleep: {sleep.total_sleep_hours:.1f} hours") # Get last night's sleep sleep = client.get_last_night_sleep() # Get sleep for date range sleep_data = client.get_sleep_range( start_date=date(2025, 1, 1), end_date=date(2025, 1, 7) )
Activities
# Get recent activities activities = client.get_recent_activities(limit=5) for activity in activities: print(f"{activity.activity_name}: {activity.distance_km:.1f} km") # Get activities with filters activities = client.get_activities( start_date=date(2025, 1, 1), end_date=date(2025, 1, 31), activity_type="running", limit=20 ) # Get single activity by ID activity = client.get_activity(12345678)
Heart Rate
# Get heart rate data for a day hr = client.get_heart_rate() print(f"Resting HR: {hr.resting_heart_rate} bpm") # Get just resting heart rate resting_hr = client.get_resting_heart_rate(date(2025, 1, 15))
Stress & Body Battery
# Get stress data stress = client.get_stress() print(f"Avg stress: {stress.avg_stress_level}") # Get body battery data battery = client.get_body_battery()
Steps
# Get detailed step data steps = client.get_steps() print(f"Total: {steps.total_steps}, Goal: {steps.step_goal}") # Get just total steps total = client.get_total_steps(date(2025, 1, 15))
Body Composition
# Get latest weight weight = client.get_latest_weight() print(f"Weight: {weight.weight_kg} kg") # Get weight for specific date weight = client.get_weight(date(2025, 1, 15)) # Get full body composition body = client.get_body_composition()
Hydration & Respiration
# Get hydration data hydration = client.get_hydration() print(f"Intake: {hydration.total_intake_ml} ml") # Get respiration data resp = client.get_respiration() print(f"Avg breathing: {resp.avg_waking_respiration} breaths/min")
Comprehensive Reports
# Get health snapshot (all metrics for a day) snapshot = client.get_health_snapshot() # Returns: daily_summary, sleep, heart_rate, stress, steps, hydration, respiration # Get weekly health report with trends report = client.get_weekly_health_report() # Returns: activities summary, sleep stats, steps stats, HR trends, stress trends # Export data for date range data = client.export_data( start_date=date(2025, 1, 1), end_date=date(2025, 1, 31), include_activities=True, include_sleep=True, include_daily=True )
Health Check Query
When a user asks "How did I sleep?" or "What's my health summary?": garmer snapshot --json
Activity Analysis
When a user asks about workouts or exercise: garmer activities --limit 10
Trend Analysis
When analyzing health trends over time: garmer export --days 30 --output health_data.json Then process the JSON file with Python for analysis.
Data Types Available
Activities: Running, cycling, swimming, strength training, etc. Sleep: Duration, phases (deep, light, REM), score, HRV Heart Rate: Resting HR, samples, zones Stress: Stress levels, body battery Steps: Total steps, distance, floors Body Composition: Weight, body fat, muscle mass Hydration: Water intake tracking Respiration: Breathing rate data
Error Handling
If not authenticated: Not logged in. Use 'garmer login' first. If session expired, re-authenticate: garmer login
Environment Variables
GARMER_TOKEN_DIR: Custom directory for token storage GARMER_LOG_LEVEL: Set logging level (DEBUG, INFO, WARNING, ERROR) GARMER_CACHE_ENABLED: Enable/disable data caching (true/false)
References
For detailed API documentation and MoltBot integration examples, see references/REFERENCE.md.
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Scripts1 Files
- SKILL.md
- README.md
- references/REFERENCE.md
- examples/basic_usage.py
- examples/moltbot_integration.py
- pyproject.toml