dvcrn Fitbit Fitness Insights
Get AI-powered insights from your Fitbit data. Query your fitness metrics, analyze trends, and ask questions about your activity.
Features
- 📊 Daily activity summaries (steps, calories, distance, active minutes)
- 💓 Heart rate data and zones
- 😴 Sleep tracking and analysis
- 🏃 Workout/activity logs
- 📈 Weekly and trend analysis
- 🤖 AI-powered insights and Q&A
Prerequisites
Requires: Fitbit OAuth access token
Setup steps in references/fitbit-oauth-setup.md
Commands
Get Profile
FITBIT_ACCESS_TOKEN="..." python3 scripts/fitbit_api.py profile
Daily Activity
python3 scripts/fitbit_api.py daily [date]
# Examples:
python3 scripts/fitbit_api.py daily # Today
python3 scripts/fitbit_api.py daily 2026-02-08 # Specific date
Returns: steps, distance, calories, active minutes (very/fairly/lightly/sedentary), floors
Steps Range
python3 scripts/fitbit_api.py steps <start_date> <end_date>
Example:
python3 scripts/fitbit_api.py steps 2026-02-01 2026-02-07
Returns: total steps, average steps, daily breakdown
Heart Rate
python3 scripts/fitbit_api.py heart [date]
Returns: resting heart rate, heart rate zones with minutes in each zone
Sleep Data
python3 scripts/fitbit_api.py sleep [date]
Returns: duration, efficiency, start/end times, sleep stages
Logged Activities
python3 scripts/fitbit_api.py activities [date]
Returns: workouts/activities logged (name, duration, calories, distance)
Weekly Summary
python3 scripts/fitbit_api.py weekly
Returns: 7-day summary of steps and key metrics
AI Insights Usage
When user asks fitness questions, use the API to fetch relevant data, then provide insights:
Example queries:
- "How did I sleep last night?" → fetch sleep data, analyze quality
- "Did I hit my step goal this week?" → fetch weekly summary, compare to goals
- "What was my average heart rate during workouts?" → fetch heart + activities, analyze
- "Am I more active on weekdays or weekends?" → fetch range data, compare patterns
Analysis approach:
- Identify what data is needed
- Fetch via appropriate API command
- Analyze the data
- Provide insights in conversational format
Example Responses
User: "How did I do this week?"
Agent:
- Fetch weekly summary
- Fetch recent sleep data
- Respond: "You had a solid week! Averaged 8,234 steps/day (up 12% from last week). Hit your 10k step goal 4 out of 7 days. Sleep averaged 7.2 hours with 85% efficiency. CrossFit sessions on Mon/Wed/Fri looking consistent!"
User: "Did I exercise today?"
Agent:
- Fetch daily activities
- Fetch daily activity summary (active minutes)
- Respond: "Yes! You logged a CrossFit session this morning (45 min, 312 calories). Plus 28 very active minutes total for the day."
Data Insights to Look For
- Trends: Week-over-week changes, consistency patterns
- Goals: Compare to 10k steps, exercise frequency, sleep targets
- Correlations: Sleep quality vs activity, rest days vs performance
- Anomalies: Unusual spikes or drops
- Achievements: Personal bests, streaks, milestones
Token Management
The skill automatically loads tokens from /root/clawd/fitbit-config.json and refreshes them when expired (every 8 hours).
Auto-refresh: Tokens are refreshed automatically - no manual intervention needed!
Manual refresh (if needed):
python3 scripts/refresh_token.py force
Override with environment variable:
export FITBIT_ACCESS_TOKEN="manual_token"
Error Handling
- Missing token: Prompt user to set FITBIT_ACCESS_TOKEN
- API errors: Check token validity, may need refresh
- No data: Some days may have no logged activities or missing metrics
See references/fitbit-oauth-setup.md for token management.