health

Store per-user health data in a per-user SQLite database for time-series logging.

10|Updated Feb 17, 2026
One-click install
npx skills add https://github.com/istota-project/istota --skill health-istota-project
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: health
Source: https://github.com/istota-project/istota/tree/main/src/istota/skills/health
Command: npx skills add https://github.com/istota-project/istota --skill health-istota-project

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Health data is highly personal and often scattered across apps and documents. This skill provides a per-user SQLite-backed store and a CLI to log body stats, track biomarkers, manage lab panels, and summarize Garmin-derived metrics, enabling coherent time-series health insights while preserving privacy.

Core Features & Use Cases

  • Time-series logging for metrics like weight, blood pressure, heart rate, and biomarker values.
  • Lab panel management: add panels, biomarkers, and view trends; supports uploading and exporting data.
  • Privacy-first operation: per-user database inside the workspace and sandbox/deferred write support.

Quick Start

Log a weight measurement to start building a per-user health timeline.

Frequently Asked Questions about health

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I log body stats and biomarker trends in a per-user SQLite database?

Log body stats and biomarker trends using a per-user SQLite database that enables time-series recording of measurements like weight and blood pressure. The sandbox-aware write pipeline normalizes units and enforces strict privacy for each user's health data.

Can I track Garmin-derived health metrics in a privacy-first time-series store?

Garmin-derived health metrics are supported for time-series tracking within a privacy-first store. The system uses a per-user database inside the workspace with sandbox and deferred write support to ensure strict environment-based data handling.

How does unit normalization work when logging health measurements across different lab panels?

Unit normalization is enforced through strict environment-based configuration during the logging pipeline. This ensures that body stats, biomarkers, and lab panel measurements are consistently standardized for accurate time-series trend generation.

What's the best way to generate trend and summary views from scattered health data?

Generate trend and summary views by consolidating scattered health data into a per-user SQLite store. The system applies a secure write pipeline to log body stats and biomarkers, producing coherent time-series health insights while preserving data privacy.

Do I need a sandbox environment to securely write and track personal health biomarkers?

A sandbox-aware write pipeline is required to securely track personal health biomarkers. This deferred write support enforces strict privacy and secure data handling inside the per-user workspace database for all logged measurements.