fitbit

# 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 ```bash FITBIT_ACCESS_TOKEN="..." python3 scripts/fitbit_api.py profile ``` ### Daily Activity ```bash 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 ```bash python3 scripts/fitbit_api.py steps <start_date> <end_date> ``` Example: ```bash python3 scripts/fitbit_api.py steps 2026-02-01 2026-02-07 ``` Returns: total steps, average steps, daily breakdown ### Heart Rate ```bash python3 scripts/fitbit_api.py heart [date] ``` Returns: resting heart rate, heart rate zones with minutes in each zone ### Sleep Data ```bash python3 scripts/fitbit_api.py sleep [date] ``` Returns: duration, efficiency, start/end times, sleep stages ### Logged Activities ```bash python3 scripts/fitbit_api.py activities [date] ``` Returns: workouts/activities logged (name, duration, calories, distance) ### Weekly Summary ```bash 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):** ```bash python3 scripts/refresh_token.py force ``` **Override with environment variable:** ```bash 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.