--- name: fitbit-5c6da77b0fc4 description: Fitbit fitness data integration. Use when the user wants fitness insights, workout summaries, step counts, heart rate data, sleep analysis, or to ask questions about their Fitbit activity data. Provides AI-powered analysis of fitness metrics. --- # 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 ``` 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. --- Source attribution: modbender Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/fitbit-insights/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 skill-library-mcp contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: modbender Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/fitbit-insights/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Healthcare & Life Sciences, Sales Roles: Customer Success Manager, Data Analyst, Health Informatics Specialties: API Tools: Fitbit Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 4eb1e644e7bc0d7ed2f11504120d93cb0b23aca93d9cc8352b361c8b587b0970