gh-asleep-ai-sleeptrack-skills-sleeptrack-skills/skills/sleeptrack-be/SKILL.md

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sleeptrack-be

This skill provides comprehensive backend REST API integration for Asleep sleep tracking platform. Use this skill when building server-side applications, API proxies for mobile apps, webhook event handlers, cross-platform backends (React Native, Flutter), analytics dashboards, or multi-tenant sleep tracking systems. Covers authentication, user management, session retrieval, statistics, webhook integration, and production-ready patterns with code examples in Python, Node.js, and curl.

Sleeptrack Backend API Integration

Overview

This skill provides comprehensive guidance for integrating the Asleep REST API into backend applications. It covers server-side user management, session data retrieval, statistics aggregation, webhook event handling, and production-ready patterns for building robust sleep tracking backends.

Use this skill when:

• Building backend/server-side sleep tracking integrations
• Creating API proxies for mobile applications
• Implementing webhook handlers for real-time sleep data
• Developing cross-platform backends (React Native, Flutter)
• Building analytics dashboards and reporting systems
• Creating multi-tenant sleep tracking applications
• Integrating sleep data with other health platforms

Quick Start

1. Get Your API Key

1. Sign up at https://dashboard.asleep.ai
2. Generate an API key for your application
3. Store securely in environment variables (never commit to version control)

2. Basic Authentication

All API requests require the x-api-key header:

curl:

curl -X GET "https://api.asleep.ai/ai/v1/users/USER_ID" \
  -H "x-api-key: YOUR_API_KEY"

Python:

import requests

headers = {"x-api-key": "YOUR_API_KEY"}
response = requests.get(
    "https://api.asleep.ai/ai/v1/users/USER_ID",
    headers=headers
)

Node.js:

const axios = require('axios');

const response = await axios.get(
  'https://api.asleep.ai/ai/v1/users/USER_ID',
  {
    headers: { 'x-api-key': 'YOUR_API_KEY' }
  }
);

API Client Structure

Build a reusable API client to handle authentication, error handling, and common operations.

Key Components:

• Base URL configuration (https://api.asleep.ai)
• API key authentication in headers
• Error handling for common HTTP status codes (401, 403, 404)
• Request methods for all API endpoints
• Session management with persistent connections

For complete implementations:

• Python: See references/python_client_implementation.md
• Node.js: See references/nodejs_client_implementation.md
• REST API details: See references/rest_api_reference.md

Basic Client Structure:

class AsleepClient:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.asleep.ai"
        self.session = requests.Session()
        self.session.headers.update({"x-api-key": api_key})

    def _request(self, method: str, path: str, **kwargs):
        # Handle authentication and errors
        # See python_client_implementation.md for full code
        pass

User Management

Creating Users

Create users before tracking sleep. User IDs are managed by your application.

# Create user with metadata
user_id = client.create_user(metadata={
    "birth_year": 1990,
    "gender": "male",
    "height": 175.5,  # cm
    "weight": 70.0    # kg
})

Available Metadata Fields:

• birth_year (Integer): Birth year
• birth_month (Integer): 1-12
• birth_day (Integer): 1-31
• gender (String): male, female, non_binary, other, prefer_not_to_say
• height (Float): Height in cm (0-300)
• weight (Float): Weight in kg (0-1000)

Retrieving User Information

user_data = client.get_user(user_id)
# Returns: user_id, to_be_deleted status, last_session_info, metadata

Response includes:

• User ID and deletion status
• Last session information (if available)
• User metadata (demographic information)

Deleting Users

Permanently removes user and all associated data (sessions, reports).

client.delete_user(user_id)

For detailed examples and response structures:

• See references/rest_api_reference.md (User Management section)
• See references/python_client_implementation.md or references/nodejs_client_implementation.md

Session Management

Retrieving Session Details

Get comprehensive sleep analysis for a specific session.

session = client.get_session(
    session_id="session123",
    user_id="user123",
    timezone="America/New_York"
)

# Access key metrics
print(f"Sleep efficiency: {session['stat']['sleep_efficiency']:.1f}%")
print(f"Total sleep time: {session['stat']['sleep_time']}")
print(f"Sleep stages: {session['session']['sleep_stages']}")

Sleep Stage Values:

• -1: Unknown/No data
• 0: Wake
• 1: Light sleep
• 2: Deep sleep
• 3: REM sleep

Key Metrics:

• sleep_efficiency: (Total sleep time / Time in bed) × 100%
• sleep_latency: Time to fall asleep (seconds)
• waso_count: Wake after sleep onset episodes
• time_in_bed: Total time in bed (seconds)
• time_in_sleep: Actual sleep time (seconds)
• time_in_light/deep/rem: Stage durations (seconds)

Listing Sessions

Retrieve multiple sessions with date filtering and pagination.

sessions = client.list_sessions(
    user_id="user123",
    date_gte="2024-01-01",
    date_lte="2024-01-31",
    limit=50,
    order_by="DESC"
)

Pagination Example:

all_sessions = []
offset = 0
limit = 100

while True:
    result = client.list_sessions(
        user_id="user123",
        offset=offset,
        limit=limit
    )
    sessions = result['sleep_session_list']
    all_sessions.extend(sessions)

    if len(sessions) < limit:
        break
    offset += limit

Deleting Sessions

client.delete_session(session_id="session123", user_id="user123")

For detailed session data structures and examples:

• See references/rest_api_reference.md (Session Management section)
• See client implementation references for language-specific examples

Statistics and Analytics

Average Statistics

Get aggregated sleep metrics over a time period (max 100 days).

stats = client.get_average_stats(
    user_id="user123",
    start_date="2024-01-01",
    end_date="2024-01-31",
    timezone="UTC"
)

avg = stats['average_stats']
print(f"Average sleep time: {avg['sleep_time']}")
print(f"Average efficiency: {avg['sleep_efficiency']:.1f}%")
print(f"Light sleep ratio: {avg['light_ratio']:.1%}")
print(f"Number of sessions: {len(stats['slept_sessions'])}")

Returned Metrics:

• Average sleep time, bedtime, wake time
• Average sleep efficiency
• Sleep stage ratios (light, deep, REM)
• List of sessions included in calculation

For trend analysis and advanced analytics:

• See references/python_client_implementation.md (Analytics section)
• See references/rest_api_reference.md (Statistics section)

Webhook Integration

Webhooks enable real-time notifications for sleep session events.

Webhook Event Types

1. INFERENCE_COMPLETE: Incremental sleep data during tracking (every 5-40 minutes)
2. SESSION_COMPLETE: Final comprehensive sleep analysis when session ends

Setting Up Webhook Endpoint

Basic Structure:

@app.route('/asleep-webhook', methods=['POST'])
def asleep_webhook():
    # 1. Verify authentication (x-api-key header)
    # 2. Parse event data
    # 3. Handle event type (INFERENCE_COMPLETE or SESSION_COMPLETE)
    # 4. Process and store data
    # 5. Return 200 response
    pass

Key Implementation Points:

• Verify x-api-key header matches your API key
• Validate x-user-id header
• Handle both INFERENCE_COMPLETE and SESSION_COMPLETE events
• Implement idempotency (check if event already processed)
• Process asynchronously for better performance
• Return 200 status immediately

For complete webhook implementations:

• Python (Flask): See references/webhook_implementation_guide.md
• Node.js (Express): See references/webhook_implementation_guide.md
• Webhook payloads: See references/webhook_reference.md

Webhook Best Practices

Idempotency: Check if webhook event was already processed to avoid duplicates.

Asynchronous Processing: Queue webhook events for background processing and respond immediately.

Error Handling: Return 200 even if processing fails internally to prevent retries.

For detailed patterns:

• See references/webhook_implementation_guide.md
• See references/production_patterns.md (Background Jobs section)

Common Backend Patterns

1. API Proxy for Mobile Apps

Create a backend proxy to:

• Hide API keys from mobile clients
• Add custom authentication
• Implement business logic
• Track usage and analytics

Key endpoints:

• POST /api/users - Create Asleep user for authenticated app user
• GET /api/sessions/{id} - Proxy session retrieval with auth
• GET /api/sessions - List sessions with filtering
• GET /api/statistics - Get aggregated statistics

For complete implementation:

• See references/python_client_implementation.md (API Proxy section)

2. Analytics Dashboard Backend

Aggregate and analyze sleep data across multiple users:

• Calculate comprehensive sleep scores
• Generate weekly/monthly reports
• Analyze cohort sleep patterns
• Provide personalized insights

Key features:

• Sleep score calculation (efficiency + consistency + duration)
• Trend analysis over time
• Multi-user aggregation
• Report generation

For complete implementation:

• See references/python_client_implementation.md (Analytics section)

3. Multi-Tenant Application

Manage sleep tracking for multiple organizations or teams:

• Organization-level user management
• Aggregated organization statistics
• Role-based access control
• Per-organization settings

For complete implementation:

• See references/python_client_implementation.md (Multi-Tenant section)

Error Handling

Common Error Codes

• 401 Unauthorized: Invalid API key
• 403 Forbidden: Rate limit exceeded or insufficient permissions
• 404 Not Found: Resource does not exist
• 422 Unprocessable Entity: Invalid request parameters

Retry Logic with Exponential Backoff

def retry_with_exponential_backoff(func, max_retries=3, base_delay=1.0):
    for attempt in range(max_retries):
        try:
            return func()
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 403 and "rate limit" in str(e):
                if attempt < max_retries - 1:
                    delay = min(base_delay * (2 ** attempt), 60.0)
                    time.sleep(delay)
                    continue
            raise

Custom Exception Classes

class AsleepAPIError(Exception):
    """Base exception for Asleep API errors"""
    pass

class RateLimitError(AsleepAPIError):
    """Rate limit exceeded"""
    pass

class ResourceNotFoundError(AsleepAPIError):
    """Resource not found"""
    pass

For comprehensive error handling:

• See references/python_client_implementation.md or references/nodejs_client_implementation.md
• See references/production_patterns.md (Error Recovery section)

Testing

Local Webhook Testing

Use ngrok to expose local server for webhook testing:

# Start local server
python app.py  # or npm start

# Expose with ngrok
ngrok http 5000

# Use ngrok URL in webhook configuration
# Example: https://abc123.ngrok.io/asleep-webhook

Mock API Responses

@patch('requests.Session.request')
def test_create_user(mock_request):
    mock_response = Mock()
    mock_response.json.return_value = {
        "result": {"user_id": "test_user_123"}
    }
    mock_request.return_value = mock_response

    user_id = client.create_user()
    assert user_id == "test_user_123"

For complete testing examples:

• See references/python_client_implementation.md (Testing section)

Production Best Practices

Security

1. API Key Management:

   • Store in environment variables or secret management system
   • Never commit to version control
   • Rotate keys periodically
   • Use different keys for dev/staging/production

2. Webhook Security:

   • Verify x-api-key header
   • Use HTTPS endpoints only
   • Implement rate limiting
   • Log all webhook attempts

3. User Data Privacy:

   • Encrypt sensitive data at rest
   • Implement proper access controls
   • Handle data deletion requests
   • Comply with GDPR/CCPA

Performance

1. Caching: Cache immutable session data
2. Rate Limiting: Protect your backend from overload
3. Connection Pooling: Reuse HTTP connections
4. Batch Processing: Process multiple requests in parallel

Monitoring

1. Logging: Structured logging for all API requests
2. Metrics: Track request duration, error rates, throughput
3. Health Checks: Implement /health, /ready, /live endpoints
4. Alerting: Alert on error rate spikes or API failures

Deployment

1. Configuration: Environment-based settings with validation
2. Health Checks: Support Kubernetes liveness/readiness probes
3. Graceful Shutdown: Handle termination signals properly
4. Error Recovery: Circuit breaker pattern for API failures

For comprehensive production patterns:

• Caching strategies: See references/production_patterns.md
• Rate limiting: See references/production_patterns.md
• Monitoring: See references/production_patterns.md
• Deployment: See references/production_patterns.md

Resources

Reference Documentation

This skill includes comprehensive reference files:

• references/python_client_implementation.md: Complete Python client with all methods, analytics classes, and examples
• references/nodejs_client_implementation.md: Complete Node.js client with Express integration
• references/webhook_implementation_guide.md: Full webhook handlers in Python and Node.js with best practices
• references/rest_api_reference.md: Complete REST API endpoint documentation with request/response examples
• references/webhook_reference.md: Webhook integration guide with payload structures
• references/production_patterns.md: Caching, rate limiting, monitoring, deployment, and performance optimization

To access detailed information:

Read references/python_client_implementation.md
Read references/nodejs_client_implementation.md
Read references/webhook_implementation_guide.md
Read references/rest_api_reference.md
Read references/webhook_reference.md
Read references/production_patterns.md

Official Documentation

• Main Documentation: https://docs-en.asleep.ai
• API Basics: https://docs-en.asleep.ai/docs/api-basics.md
• Webhook Guide: https://docs-en.asleep.ai/docs/webhook.md
• Dashboard: https://dashboard.asleep.ai
• LLM-Optimized Reference: https://docs-en.asleep.ai/llms.txt

Related Skills

• sleeptrack-foundation: Core concepts, authentication, data structures, and platform-agnostic patterns
• sleeptrack-ios: iOS SDK integration for native iOS applications
• sleeptrack-android: Android SDK integration for native Android applications

Support

For technical support and API issues:

• Check Dashboard for API usage and status
• Review error logs and response codes
• Contact support through Asleep Dashboard