Does this skill support writing data back to devices?

Yes, it includes operations for posting activities, nutrition logs, and planned workouts to supported providers like Wahoo, Fitbit, and Apple Health.

How is the data normalized across different manufacturers?

The skill utilizes Terra's normalization engine to map diverse raw data into standard JSON structures for metrics like calories, heart rate, sleep stages, and macros.

What is the recommended strategy for daily metric updates?

For daily, body, and nutrition metrics that update multiple times per day, the skill implements an 'upsert' or 'overwrite' strategy to maintain the most current aggregate data.

How does it handle large historical data requests?

For requests exceeding 28 days, the skill automatically switches to asynchronous processing, sending the results via webhooks to ensure stability and performance.

Which wearable brands are supported by this skill?

The skill supports over 150 providers via the Terra API, including major brands like Garmin, Fitbit, Oura, WHOOP, Polar, Withings, and Apple Health.

Terra Health Data Integration

Name: Terra Health Data Integration
Author: adaptationio

byadaptationio

Desarrollo de API

Integrates and manages health data from 150+ wearable devices using the Terra API with standardized data normalization.

Acerca de

This skill provides a robust framework for developers to fetch, manage, and synchronize health metrics from a vast ecosystem of wearable devices including Garmin, Oura, and Fitbit. By utilizing the Terra API, it simplifies the complexity of multi-device integration into a single, normalized schema. The skill covers a wide range of data types—including activity sessions, sleep stages, body composition, and nutrition—while providing clear patterns for historical backfilling, asynchronous webhook handling, and database synchronization. It is ideal for building fitness applications, wellness dashboards, or biometric research tools that require consistent, cross-platform health data.

Características Principales

Connects to 150+ wearable brands and health apps through a unified interface.
Provides SQL schema patterns for robust data persistence and session management.
Normalizes fragmented data into consistent Activity, Sleep, Body, Daily, and Nutrition schemas.
Automates historical data backfilling with intelligent sync/async switching based on date range.
0 GitHub stars
Supports bi-directional data flow for posting activities and plans to select wearable providers.

Casos de Uso

Building automated health monitoring systems for coaching or medical research applications.
Developing cross-platform fitness dashboards that aggregate data from multiple user devices.
Syncing real-time biometric data into personalized nutrition or recovery recommendation engines.

Acerca de

Características Principales

Connects to 150+ wearable brands and health apps through a unified interface.
Provides SQL schema patterns for robust data persistence and session management.
Normalizes fragmented data into consistent Activity, Sleep, Body, Daily, and Nutrition schemas.
Automates historical data backfilling with intelligent sync/async switching based on date range.
0 GitHub stars
Supports bi-directional data flow for posting activities and plans to select wearable providers.

Casos de Uso

Building automated health monitoring systems for coaching or medical research applications.
Developing cross-platform fitness dashboards that aggregate data from multiple user devices.
Syncing real-time biometric data into personalized nutrition or recovery recommendation engines.