Oura Analytics - OpenClaw Skill

Production-grade Oura Ring data integration for OpenClaw
Fetch sleep scores, readiness, activity, HRV, and trends from Oura Cloud API. Generate automated health reports and trigger-based alerts.

Features

✅ Oura Cloud API Integration - Personal Access Token authentication
✅ Sleep Analytics - Score, duration, efficiency, REM/deep stages
✅ Readiness Tracking - Recovery score, HRV balance, temperature
✅ Activity Metrics - Steps, calories, MET minutes
✅ Trend Analysis - Moving averages, correlations, anomaly detection
✅ Automated Alerts - Low readiness/sleep notifications via Telegram

Version

Current: v0.1.2

See CHANGELOG for version history.

Why This Exists

OpenClaw needs access to Oura Ring health data for:

• Daily morning briefings ("How did I sleep?")
• Correlating recovery with productivity/calendar
• Automated alerts for low recovery days
• Weekly/monthly health trend reports

This skill provides:

• Simple Python API client for Oura Cloud API v2
• Trend analysis and correlation tools
• Threshold-based alerting system
• Report generation templates

Installation

1. Get Oura Personal Access Token

1. Go to https://cloud.ouraring.com/personal-access-tokens
2. Create new token (select all scopes)
3. Copy token to secrets file:

echo 'OURA_API_TOKEN="your_token_here"' >> ~/.config/systemd/user/secrets.conf

2. Install the skill

git clone https://github.com/kesslerio/oura-analytics-openclaw-skill.git ~/.openclaw/skills/oura-analytics
pip install -r requirements.txt

3. Add to OpenClaw's TOOLS.md

### oura-analytics
- Fetch Oura Ring metrics (sleep, readiness, activity, HRV)
- Generate health reports and correlations
- Set up automated alerts for low recovery
- Usage: `python ~/.openclaw/skills/oura-analytics/scripts/oura_api.py sleep --days 7`

Usage Examples

Note: For Python imports, set PYTHONPATH to the scripts/ folder:

export PYTHONPATH="$(pwd)/scripts"

Fetch Sleep Data

# Last 7 days
python scripts/oura_api.py sleep --days 7

Get Readiness Summary

python scripts/oura_api.py readiness --days 7

Generate Reports

# Weekly summary (last 7 days)
python scripts/weekly_report.py --days 7
 
# Monthly trends (last 30 days)
python scripts/weekly_report.py --days 30

Trigger Alerts

# Check for low readiness and send Telegram notification
python scripts/alerts.py --days 7 --readiness 60 --efficiency 80 --telegram

Generate Hybrid Morning Briefing

# Daily hybrid report (morning briefing + 7-day trends)
python scripts/oura_briefing.py --format hybrid

Example hybrid output:

🌅 *Morning Briefing — Jan 22*
────────────────────────
💤 *Sleep*: 6h 47m (↑75min vs avg) ⚠️
⚡ *Readiness*: 80 (stable) ✅
*Drivers*: recovery_index, body_temperature
*Recovery*: 🟡 YELLOW
*Rec*: Moderate day. Avoid heavy training.

*📊 7-Day Trends*
────────────────────────
*Sleep Score*: `89.5` ↓
*Readiness*: `77.1` ↑
• *7.3h* sleep • *89.7%* eff • *21ms* HRV

*Recent*: 01-20 → `87.4`/`73` • 01-21 → `90.4`/`80`

Baseline & Comparison Analysis

# Compare last 7 days vs 30-day baseline
python scripts/baseline.py --current-days 7 --baseline-days 30
 
# View 90-day baseline statistics
python scripts/baseline.py --baseline-only --baseline-days 90
 
# JSON output for programmatic use
python scripts/baseline.py --json

Example output:

📈 Current vs Baseline (Last 7d vs 30d baseline)

   ↗️ Sleep Score: 89.5 (+10.6, z=0.53)
      Above baseline

   ➡️ Readiness: 77.1 (+1.0, z=0.15)
      Within baseline

   ↗️ Sleep Duration: 7.3 (+1.4, z=0.52)
      Above baseline

   ↗️ Efficiency: 89.7 (+6.8, z=0.52)
      Above baseline

✅ All metrics within or above baseline range

Interpretation:

• z-score: Standard deviations from baseline mean
  • z > 1.5: 🔥 Well above baseline
  • 0.5 < z < 1.5: ↗️ Above baseline
  • -0.5 < z < 0.5: ➡️ Within baseline
  • -1.5 < z < -0.5: ↘️ Below baseline
  • z < -1.5: ⚠️ Well below baseline (needs attention)
• Baseline range: P25-P75 (middle 50% of your historical data)
• Sample size: Number of days used to calculate baseline

Core Workflows

1. Morning Health Check

from oura_api import OuraClient, OuraAnalyzer
 
client = OuraClient(token=os.getenv("OURA_API_TOKEN"))
sleep_data = client.get_sleep(start_date="2026-01-18", end_date="2026-01-18")
today = sleep_data[0] if sleep_data else {}
 
if today:
    print(f"Sleep Score: {today.get('score', 'N/A')}/100")
    print(f"Total Sleep: {today.get('total_sleep_duration', 0)/3600:.1f}h")
    print(f"REM: {today.get('rem_sleep_duration', 0)/3600:.1f}h")
    print(f"Deep: {today.get('deep_sleep_duration', 0)/3600:.1f}h")

2. Recovery Tracking

readiness = client.get_readiness(start_date="2026-01-11", end_date="2026-01-18")
avg_readiness = sum(d.get('score', 0) for d in readiness) / len(readiness) if readiness else 0
print(f"7-day avg readiness: {avg_readiness:.0f}")

3. Trend Analysis

from oura_api import OuraAnalyzer
 
analyzer = OuraAnalyzer(sleep_data, readiness_data)
avg_sleep = analyzer.average_metric(sleep_data, "score")
avg_readiness = analyzer.average_metric(readiness_data, "score")
print(f"Avg Sleep Score: {avg_sleep}")
print(f"Avg Readiness Score: {avg_readiness}")

API Client Reference

OuraClient

client = OuraClient(token="your_token")
 
# Sleep data (date range required)
sleep = client.get_sleep(start_date="2026-01-01", end_date="2026-01-16")
 
# Readiness data
readiness = client.get_readiness(start_date="2026-01-01", end_date="2026-01-16")
 
# Activity data
activity = client.get_activity(start_date="2026-01-01", end_date="2026-01-16")
 
# HRV trends
hrv = client.get_hrv(start_date="2026-01-01", end_date="2026-01-16")

OuraAnalyzer

from oura_api import OuraClient, OuraAnalyzer
 
client = OuraClient(token="your_token")
sleep = client.get_sleep(start_date="2026-01-01", end_date="2026-01-16")
readiness = client.get_readiness(start_date="2026-01-01", end_date="2026-01-16")
 
analyzer = OuraAnalyzer(sleep_data=sleep, readiness_data=readiness)
 
# Average metrics
avg_sleep = analyzer.average_metric(sleep, "score")
avg_readiness = analyzer.average_metric(readiness, "score")
 
# Trend analysis
trend = analyzer.trend(sleep, "score", days=7)
 
# Summary
summary = analyzer.summary()

OuraReporter

from oura_api import OuraClient, OuraReporter
 
client = OuraClient(token="your_token")
reporter = OuraReporter(client)
 
# Generate weekly report
report = reporter.generate_report(report_type="weekly", days=7)
print(json.dumps(report, indent=2))

Metrics Reference

See references/metrics.md for full definitions.

Architecture

• scripts/oura_api.py - Oura Cloud API v2 client with OuraAnalyzer and OuraReporter classes
• scripts/alerts.py - Threshold-based alerting CLI
• scripts/weekly_report.py - Weekly report generator
• scripts/data_manager.py - Data storage and privacy controls
• scripts/oura_data.py - Data management CLI
• scripts/schema.py - Canonical data structures with unit normalization
• references/ - API docs, metric definitions

Data Management & Privacy

What Data is Stored

All data is stored locally in ~/.oura-analytics/:

~/.oura-analytics/
├── cache/                  # Cached API responses (cleanup is manual)
│   ├── sleep/             # Sleep records by date (YYYY-MM-DD.json)
│   ├── daily_readiness/   # Readiness records
│   └── daily_activity/    # Activity records
├── events.jsonl           # User-logged events (optional)
├── config.yaml            # User preferences (optional)
└── alert_state.json       # Alert tracking (optional)

No data is sent to third parties. All Oura data stays on your local machine.

View Storage Info

python scripts/oura_data.py info

Output:

Data directory: /home/user/.oura-analytics
Total size: 187.1 KB

Cache:
  Size: 187.1 KB
  Files: 21
  sleep: 7 files, 46.4 KB, 2026-01-14 to 2026-01-20
  daily_readiness: 7 files, 3.6 KB, 2026-01-14 to 2026-01-20
  daily_activity: 7 files, 137.2 KB, 2026-01-14 to 2026-01-20

Export Data (Backup)

Export all local data to a single JSON file:

# Full backup
python scripts/oura_data.py export --output backup.json
 
# Compressed tarball
python scripts/oura_data.py export --output backup.tar.gz --format tar.gz
 
# Export events only
python scripts/oura_data.py export-events --output events.csv --format csv

Clear Data (Privacy)

# Clear cache only (keeps events/config)
python scripts/oura_data.py clear-cache --confirm
 
# Clear specific endpoint
python scripts/oura_data.py clear-cache --endpoint sleep --confirm
 
# Clear events
python scripts/oura_data.py clear-events --confirm
 
# Clear ALL local data
python scripts/oura_data.py clear-all --confirm

Important: All clear commands require --confirm flag to prevent accidental deletion.

Automatic Cleanup

Delete cached data older than 90 days:

# Default: 90 days
python scripts/oura_data.py cleanup
 
# Custom retention period
python scripts/oura_data.py cleanup --days 180

GDPR Compliance

• ✅ Data ownership: You own your data (local storage only)
• ✅ Data retention: You control retention (manual cleanup)
• ✅ No data sharing: No third-party services
• ✅ Right to deletion: Clear data anytime with clear-all

Note: This skill is NOT HIPAA-compliant. Do not use for medical decision-making. Consult healthcare professionals for health concerns.

Troubleshooting

Authentication Failed

# Check token is set
echo $OURA_API_TOKEN
 
# Or use explicit token
python scripts/oura_api.py sleep --days 7 --token "your_token"

No Data Returned

# Check date range (Oura data has ~24h delay)
python scripts/oura_api.py sleep --days 10
 
# Or fetch and inspect manually
python scripts/oura_api.py sleep --days 7 | python -m json.tool | head -50

Credits

Created for production OpenClaw health tracking
Developed by @kesslerio • Part of the ClawdHub ecosystem

Powered by:

• Oura Ring - Wearable health tracker
• Oura Cloud API v2 - Official API

License

Apache 2.0