A local health data pipeline that syncs Apple Watch metrics directly to your PC via a Python-based server and iOS automation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install apple-watch
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install apple-watch using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Apple Watch Health Sync?
This skill provides a secure, local-first way to bridge the gap between Apple HealthKit and your development environment. By utilizing Openclaw Skills, users can bypass restrictive cloud ecosystems and stream their personal biometric data—including sleep stages, heart rate variability, and activity levels—directly to a local machine. It transforms your PC into a personal health hub, capable of receiving, storing, and visualizing data without intermediate cloud storage.
The core of the skill is a lightweight Flask server that receives encrypted HTTP POST requests from the Health Auto Export app on iOS. This setup ensures that your sensitive health information stays within your local network, providing developers with a queryable API and a real-time dashboard for monitoring physiological trends.
Apple Watch Health Sync Use Cases
- Creating a centralized local database of all Apple Watch health metrics for long-term archiving.
- Building custom health-aware automations that trigger based on sleep cycles or activity goals.
- Monitoring heart rate and stress levels in real-time during focused work sessions.
- Providing an AI agent with context-aware health data to offer personalized wellness insights.
How Apple Watch Health Sync Works
- The Apple Watch captures health data and syncs it to the paired iPhone's HealthKit database.
- The Health Auto Export app on the iPhone retrieves the specified metrics at set intervals.
- Data is transmitted via HTTP POST over the local Wi-Fi network to a Python server running on the user's PC.
- The server validates the request using a secure API key and appends the data to categorized JSONL files.
- The integrated dashboard or API endpoints allow for real-time visualization and data retrieval.
Apple Watch Health Sync Setup
First, ensure your iPhone and PC are on the same Wi-Fi network and you have the Health Auto Export app installed. Run the automated setup script to initialize the environment:
python scripts/setup.py
Follow these configuration steps:
- Send the generated JSON template from the
templates/folder to your iPhone and import it into Health Auto Export. - Manually add a header in the app with the key
api-keyand the value found in your.env.jsonfile. - Start the server as a persistent service. For Windows users, register a scheduled task; for macOS, use the provided launchd plist to ensure the server survives reboots.
Apple Watch Health Sync Data Schema & Taxonomy
The skill organizes data into a structured hierarchy within the local directory, using JSONL files for efficient, append-only storage. This structure is ideal for Openclaw Skills users who want to perform data analysis.
| Location | Data Type | Description |
|---|---|---|
data/metrics/ |
.jsonl |
Contains time-series data for heart_rate, sleep_analysis, and step_count. |
data/workouts/ |
.jsonl |
Detailed logs for all recorded exercise sessions. |
templates/ |
.json |
Importable configurations for the iOS automation bridge. |
.env.json |
.json |
Stores the unique API key used for secure server communication. |
Apple Watch Health Sync Advanced Features
- Local REST API endpoints for querying specific metrics with custom limit parameters.
- Dark-themed web dashboard with 30-second auto-refresh for immediate visual feedback.
- Heartbeat integration capability for AI agents to periodically check health status and notify the user.
- Persistent background execution support for Windows (Task Scheduler) and macOS (launchd).
- Minimalist dependency footprint, requiring only Python 3.8+ and Flask.
SKILL.md
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