Health Tracking for Openclaw

A comprehensive health data management system that logs physiological metrics, workouts, and uses AI vision to analyze nutritional content from food photos.

yusaku-0426
v1.0.0
Feb 24, 2026
0
495
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install health-tracking

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install health-tracking 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 Health Tracking?

The Health Tracking skill is a sophisticated data logging and analysis engine designed for the Openclaw Skills ecosystem. It enables users to maintain a detailed digital health journal by recording weight, sleep patterns, hydration levels, and exercise intensity directly into structured CSV formats.

Beyond manual entry, this skill leverages AI vision capabilities to transform meal photos into actionable nutritional data. By analyzing food images, it automatically estimates calories and macronutrients (protein, carbs, fats), allowing for a seamless integration of visual dietary tracking into a developer's daily workflow.

Health Tracking Use Cases

  • Logging daily physiological metrics like weight, sleep duration, and resting heart rate.
  • Automating nutritional analysis by submitting food photos for AI-driven calorie and macro estimation.
  • Tracking hydration levels throughout the day to ensure optimal health performance.
  • Archiving workout sessions with specific data points on duration, intensity, and caloric burn.
  • Generating end-of-day health reports to reflect on mood and daily productivity.

How Health Tracking Works

  1. The user provides health data via CLI commands or uploads a meal photo to the AI agent.
  2. For photos, the system uses vision models to analyze the dish and estimate its nutritional profile (calories, protein, carbs, fat, fiber, etc.).
  3. The health_record.js or nutrition_append.js scripts process the input data.
  4. Validated data is appended to the relevant domain-specific CSV file in the data directory.
  5. The skill organizes interactions into daily threads (e.g., in Slack or Discord) to keep logs categorized and easily accessible.

Health Tracking Setup

To integrate this into your Openclaw Skills environment, ensure you have the required scripts in your project directory:

# Example: Recording morning health metrics
node scripts/health_record.js morning --weight=70 --sleep=7 --mood=4 --hr=60

# Example: Logging a workout
node scripts/health_record.js workout --activity=running --duration=30 --intensity=3 --cal=300

Health Tracking Data Schema & Taxonomy

The skill organizes data across multiple CSV files for clean taxonomy:

File Path Description Key Columns
data/daily.csv Physiological & Mood weight_kg, sleep_hours, resting_hr, mood_1to5
data/hydration.csv Water intake amount_ml, source
data/workout.csv Exercise logs activity, duration_min, intensity_1to5, calories
data/nutrition_meals.csv Diet & Nutrition meal_type, description, photo_ref, cal, p, c, f

Health Tracking Advanced Features

  • AI Vision Analysis: Automatically extracts macro and micronutrient data from images with confidence scoring.
  • Daily Thread Management: Automatically organizes health logs within a dedicated channel using date-based threading.
  • Comprehensive Nutrition Taxonomy: Tracks detailed metrics including fiber, sugar, sodium, and saturated fats.
  • Feedback Loop: Allows for manual overrides and notes after AI analysis to ensure data accuracy.

SKILL.md


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