Building a Personal Health Dashboard: Aggregating Fitness Data with OpenClaw

One of the most practical ways to leverage your AI assistant is automating personal data collection. Community member diux shared an impressive setup this week that aggregates health and fitness data from multiple sources into a unified weekly report.

The Problem: Scattered Health Data

If you track your health seriously, you probably use multiple apps:

• Cronometer for detailed nutrition tracking
• Apple Health as your central health hub
• Hevy (or Strong, or other apps) for workout logging
• Maybe Oura, Whoop, or other wearables

The data lives in silos. Getting a complete picture requires opening multiple apps and mentally synthesizing the information.

The Solution: An OpenClaw Skill

diux built a skill that:

1. Pulls data from multiple sources - Nutrition from Cronometer, health metrics from Apple Health, workout data from Hevy
2. Aggregates into a weekly report - All the key metrics in one place
3. Presents via a dashboard - Visual representation of trends

"Had a great first weekend trying out OpenClaw. Used it to build a skill that collects all my Nutrition and Fitness data from multiple sources and gives me a weekly report / dashboard. I'm very impressed and addicted to this!" — diux

How You Could Build This

1. Data Sources

Each source needs a different approach:

• Apple Health: Use the apple-health skill or export Health data via Shortcuts
• Cronometer: Export via their web interface or use their API if you have Gold
• Hevy: Export workout history or use their API

2. Create a Skill Structure

3. Weekly Report Automation

Set up a cron job to generate reports:

{
  "cron": {
    "healthReport": {
      "schedule": "0 9 * * 1",
      "text": "Generate my weekly health report using the health-dashboard skill"
    }
  }
}

Key Metrics to Track

A useful weekly report might include:

• Nutrition: Average calories, protein, key micros
• Activity: Steps, active minutes, workouts completed
• Recovery: Sleep duration, HRV trends, resting heart rate
• Progress: Weight trend, strength PRs, running pace

Tips from the Community

• Start simple - Get one data source working before adding more
• Cache aggressively - Don't hit APIs on every request
• Mind the costs - diux mentioned running everything in Opus initially, then optimizing model usage

What's Next?

If you build something similar, share it in #showcase! The community loves seeing practical automation use cases.

Inspired by diux's showcase post in the OpenClaw Discord. Join the conversation at discord.gg/openclaw.