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:

1. Identify what data is needed
2. Fetch via appropriate API command
3. Analyze the data
4. Provide insights in conversational format

Example Responses

User: "How did I do this week?"

Agent:

1. Fetch weekly summary
2. Fetch recent sleep data
3. 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:

1. Fetch daily activities
2. Fetch daily activity summary (active minutes)
3. 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.