medtech-ontology-starter

Name: medtech-ontology-starter
Availability: InStock
Author: TextQL

Adapt a prebuilt medical-device operations ontology for TextQL/Ana with ANSI/Spark SQL.

Updated Jun 26, 2026

Search Tags:#ontology #data modeling #medical-device #TextQL #Ana

Ready to run

npx skills add https://github.com/TextQLLabs/ontology-starter-kits --skill medtech-ontology-starter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: medtech-ontology-starter
Source: https://github.com/TextQLLabs/ontology-starter-kits/tree/main/medtech
Command: npx skills add https://github.com/TextQLLabs/ontology-starter-kits --skill medtech-ontology-starter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pypdf, pdfplumber, pdf2image, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a prebuilt, governed medical-device operations ontology to adapt instead of building from scratch, saving time and reducing errors in data modeling and governance.

Core Features & Use Cases

Prebuilt Ontology: Ready-to-use entity vocabulary, metrics, classification, and governance for medical-device operations.
Fast Adaptation: Connect to your data warehouse and adjust the physical model in one file to fit your specific dataset.
Use Case: Imagine you are a medical-device manufacturer and need to quickly create an ontology for analyzing commercial, field-service, and post-market-quality data. This Skill allows you to connect your data, validate the model, and start asking governed questions in minutes.

Quick Start

Use the medtech-ontology-starter skill to connect your data and start building your ontology.

Frequently Asked Questions about medtech-ontology-starter

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

FAQPage Schema

What is a medical-device operations ontology and why do I need one for commercial data?▼

A medical-device operations ontology provides a governed vocabulary for commercial performance, installed-base reliability, and post-market quality data. It standardizes entity definitions and metrics so you can validate data models and ask governed questions without building from scratch.

Can I adapt a prebuilt ontology to connect my ERP and field-service systems?▼

Yes, you can adapt a prebuilt ontology by adjusting the physical model in a single file to fit your dataset. This allows you to connect your ERP, CRM, field-service, and post-market complaint systems to validate the model quickly.

How do I start building a medical-device ontology with TextQL and Ana?▼

To start building your ontology, connect your data warehouse, adjust the physical model file to map your specific dataset, and validate the model. This process adapts the prebuilt governed vocabulary for use with TextQL/Ana in minutes.

Does this medical-device ontology starter require specific SQL dialects or PDF parsing dependencies?▼

Yes, the ontology starter supports ANSI/Spark SQL for querying. It also requires the pypdf and pdfplumber dependencies for parsing PDFs related to post-market complaint and QMS documentation.

What is the best way to model post-market quality and safety data for medical devices?▼

The best way is to adapt a prebuilt, governed medical-device operations ontology focused on post-market quality and safety. This avoids manual data modeling errors by providing ready-to-use entity vocabulary, metrics, and governance.