Skill: rodrigouroz/whoop-health-analysis (version 1.1.0)

--- name: whoop description: Access Whoop wearable health data (sleep, recovery, strain, HRV, workouts) and generate interactive charts. Use when the user asks about sleep quality, recovery scores, strain levels, HRV trends, workout data, or wants health visualizations/graphs from their Whoop band. --- # Whoop Query health metrics from the Whoop API and generate interactive HTML charts. ## Setup (first time only) ### 1. Create a Whoop Developer App 1. Go to [developer-dashboard.whoop.com](https://developer-dashboard.whoop.com) 2. Sign in with your Whoop account credentials 3. Create a Team if prompted (any name works) 4. Click **Create App** (or go to [apps/create](https://developer-dashboard.whoop.com/apps/create)) 5. Fill in: - **App name**: anything (e.g., "Clawdbot") - **Scopes**: select ALL: `read:recovery`, `read:cycles`, `read:workout`, `read:sleep`, `read:profile`, `read:body_measurement` - **Redirect URI**: `http://localhost:9876/callback` 6. Click **Create** — you'll get a **Client ID** and **Client Secret** ### 2. Authenticate Run the OAuth login flow with your credentials: ```bash python3 scripts/whoop_auth.py login \ --client-id YOUR_CLIENT_ID \ --client-secret YOUR_CLIENT_SECRET ``` This opens a browser for Whoop authorization. Log in and approve access. Tokens are stored in `~/.clawdbot/whoop-tokens.json` and auto-refresh. Check status: `python3 scripts/whoop_auth.py status` ## Fetching Data Use `scripts/whoop_data.py` to get JSON data: ```bash # Sleep (last 7 days default) python3 scripts/whoop_data.py sleep --days 14 # Recovery scores python3 scripts/whoop_data.py recovery --days 30 # Strain/cycles python3 scripts/whoop_data.py cycles --days 7 # Workouts python3 scripts/whoop_data.py workouts --days 30 # Combined summary with averages python3 scripts/whoop_data.py summary --days 7 # Custom date range python3 scripts/whoop_data.py sleep --start 2026-01-01 --end 2026-01-15 # User profile / body measurements python3 scripts/whoop_data.py profile python3 scripts/whoop_data.py body ``` Output is JSON to stdout. Parse it to answer user questions. ## Generating Charts Use `scripts/whoop_chart.py` for interactive HTML visualizations: ```bash # Sleep analysis (performance + stages) python3 scripts/whoop_chart.py sleep --days 30 # Recovery bars (color-coded green/yellow/red) python3 scripts/whoop_chart.py recovery --days 30 # Strain & calories trend python3 scripts/whoop_chart.py strain --days 90 # HRV & resting heart rate trend python3 scripts/whoop_chart.py hrv --days 90 # Full dashboard (all 4 charts) python3 scripts/whoop_chart.py dashboard --days 30 # Save to specific file python3 scripts/whoop_chart.py dashboard --days 90 --output ~/Desktop/whoop.html ``` Charts open automatically in the default browser. They use Chart.js with dark theme, stat cards, and tooltips. ## Answering Questions | User asks | Action | |-----------|--------| | "How did I sleep?" | `whoop_data.py summary --days 7`, report sleep performance + hours | | "How's my recovery?" | `whoop_data.py recovery --days 7`, report scores + trend | | "Show me a chart for the last month" | `whoop_chart.py dashboard --days 30` | | "Is my HRV improving?" | `whoop_data.py recovery --days 30`, analyze trend | | "How much did I train this week?" | `whoop_data.py workouts --days 7`, list activities | ## Key Metrics - **Recovery** (0-100%): Green ≥67%, Yellow 34-66%, Red <34% - **Strain** (0-21): Daily exertion score based on HR - **Sleep Performance**: Actual sleep vs. sleep needed - **HRV** (ms): Higher = better recovery, track trend over time - **RHR** (bpm): Lower = better cardiovascular fitness ## Health Analysis When the user asks about their health, trends, or wants insights, use `references/health_analysis.md` for: - Science-backed interpretation of HRV, RHR, sleep stages, recovery, strain, SpO2 - Normal ranges by age and fitness level - Pattern detection (day-of-week effects, sleep debt, overtraining signals) - Actionable recommendations based on data - Red flags that suggest medical consultation ### Analysis workflow 1. Fetch data: `python3 scripts/whoop_data.py summary --days N` 2. Read `references/health_analysis.md` for interpretation framework 3. Apply the 5-step analysis: Status → Trends → Patterns → Insights → Flags 4. Always include disclaimer that this is not medical advice ## References - `references/api.md` — endpoint details, response schemas, pagination - `references/health_analysis.md` — science-backed health data interpretation guide


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)