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Official Verified data analysis Safety 4/5

garmer

Extract health and fitness data from Garmin Connect including activities, sleep, heart rate, stress, steps, and body composition. Use when the user asks about their Garmin data, fitness metrics, sleep analysis, or health insights.

Why use this skill?

Seamlessly extract and analyze your Garmin Connect health, sleep, and fitness data using OpenClaw. Get actionable insights into your workouts, recovery, and daily activity metrics.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/garrza/garmer
Or

What This Skill Does

The Garmer skill serves as a powerful bridge between your Garmin Connect fitness ecosystem and the OpenClaw AI agent. It is designed to extract, parse, and analyze comprehensive health and wellness data, including daily activity metrics, deep sleep analysis, heart rate variability, stress levels, and specific workout breakdowns. By providing a command-line interface and a robust Python API, Garmer allows users to query their physiological data seamlessly through natural language commands.

Installation

To integrate Garmer into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/garrza/garmer

Once installed, you must authenticate the tool to sync with your Garmin account. Run garmer login and follow the interactive prompts to securely store your tokens. You can verify your connection at any time by running garmer status.

Use Cases

Garmer is ideal for users looking to optimize their athletic performance or monitor long-term health trends. It excels at answering complex questions such as identifying correlations between poor sleep quality and decreased HRV, comparing performance across specific workout types, or generating weekly summaries of step counts and caloric expenditure. It is perfect for both casual users seeking general health insights and data-driven athletes who need granular metrics exported for custom analysis.

Example Prompts

  1. "Check my Garmin data from yesterday; how was my sleep quality and did it impact my heart rate variability?"
  2. "List my last 5 running activities and tell me the total distance covered for the week."
  3. "Export my health snapshot for the entire month of January to a JSON file so I can review my stress trends."

Tips & Limitations

  • Data Granularity: For the best results, use the --json flag when performing complex queries, as this allows OpenClaw to process the raw data structures more accurately.
  • Authentication: Tokens are stored locally. If you experience connection issues, run garmer login again to refresh your session credentials.
  • API Access: For advanced users, the Python GarminClient offers deeper access to metadata not always exposed through the CLI, such as specific device diagnostics or individual user profile settings.
  • Limitations: The skill is dependent on the availability of the Garmin Connect web service. If Garmin's servers are down or undergoing maintenance, data retrieval may be interrupted. Always ensure your watch is synced with the Garmin Connect app before querying, as Garmer fetches data from the cloud, not directly from the device.

Metadata

Author@garrza
Stars2387
Views2
Updated2026-03-09
View Author Profile
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-garrza-garmer": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#health#fitness#garmin#quantified-self#wellness
Safety Score: 4/5

Flags: external-api, file-read, file-write