Awesome-openclaw-skills whoop-2
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.
git clone https://github.com/sundial-org/awesome-openclaw-skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/sundial-org/awesome-openclaw-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/whoop-2" ~/.claude/skills/sundial-org-awesome-openclaw-skills-whoop-2 && rm -rf "$T"
T=$(mktemp -d) && git clone --depth=1 https://github.com/sundial-org/awesome-openclaw-skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/whoop-2" ~/.openclaw/skills/sundial-org-awesome-openclaw-skills-whoop-2 && rm -rf "$T"
skills/whoop-2/SKILL.mdWhoop
Query health metrics from the Whoop API and generate interactive HTML charts.
Setup (first time only)
1. Create a Whoop Developer App
- Go to developer-dashboard.whoop.com
- Sign in with your Whoop account credentials
- Create a Team if prompted (any name works)
- Click Create App (or go to apps/create)
- Fill in:
- App name: anything (e.g., "Clawdbot")
- Scopes: select ALL:
,read:recovery
,read:cycles
,read:workout
,read:sleep
,read:profileread:body_measurement - Redirect URI:
http://localhost:9876/callback
- Click Create — you'll get a Client ID and Client Secret
2. Authenticate
Run the OAuth login flow with your credentials:
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:
# 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:
# 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?" | , report sleep performance + hours |
| "How's my recovery?" | , report scores + trend |
| "Show me a chart for the last month" | |
| "Is my HRV improving?" | , analyze trend |
| "How much did I train this week?" | , 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
- Fetch data:
python3 scripts/whoop_data.py summary --days N - Read
for interpretation frameworkreferences/health_analysis.md - Apply the 5-step analysis: Status → Trends → Patterns → Insights → Flags
- Always include disclaimer that this is not medical advice
References
— endpoint details, response schemas, paginationreferences/api.md
— science-backed health data interpretation guidereferences/health_analysis.md