fabric-iq

Generate ontologies from semantic models and bind data sources for agent workflows.

16|1|Updated Feb 10, 2026

Search Tags:#workflow #ontology #rest api #graph #data binding #fabric iq

Ready to run

npx skills add https://github.com/PatrickGallucci/fabric-skills --skill fabric-iq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fabric-iq
Source: https://github.com/PatrickGallucci/fabric-skills/tree/main/skills/fabric-iq
Command: npx skills add https://github.com/PatrickGallucci/fabric-skills --skill fabric-iq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Az, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Solve ontology management fragmentation by providing end-to-end Fabric IQ ontology, graph, and agent workflows. It guides enterprise vocabulary management, data source bindings, and operational automation to ensure semantic consistency across Fabric workloads.

Core Features & Use Cases

Ontology design: define entity types, properties, and relationships, and bind them to OneLake data sources.
Graph and QA integration: leverage the ontology graph for traversals, queries, and grounded assistant interactions.
Data agent integration: connect ontologies to conversational agents to deliver semantically grounded responses at scale.
Use Case: generate an ontology from a semantic model and connect an ontology knowledge source to a data agent for guided Q&A.

Quick Start

Create an ontology, bind data sources, and connect a data agent using the guided workflows.

Frequently Asked Questions about fabric-iq

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

FAQPage Schema

How do I generate an ontology from a semantic model and bind it to OneLake data sources?▼

To generate an ontology from a semantic model, you define entity types, properties, and relationships, then bind them directly to OneLake data sources. This workflow ensures semantic consistency and structures your enterprise vocabulary for Fabric workloads.

What is ontology management fragmentation and how does a graph workflow solve it?▼

Ontology management fragmentation occurs when semantic definitions are scattered across systems. An end-to-end graph workflow solves this by centralizing entity relationships and traversals, enabling grounded assistant interactions and consistent enterprise vocabulary across all data agents.

Do I need a specific Fabric workspace setup to integrate ontologies with data agents?▼

Yes, integrating ontologies with data agents requires an active Fabric workspace, OneLake data available, and specific tenant settings enabled. These prerequisites allow the REST API and CLI to connect ontology knowledge sources for guided Q&A.

How do I connect an ontology knowledge source to a data agent for grounded Q&A?▼

You connect an ontology knowledge source to a data agent by applying the guided agent workflow. This binds your semantic graph to the conversational agent, delivering semantically grounded responses at scale using REST API and CLI integrations.

Can I use the REST API and CLI to automate Fabric IQ ontology workflows?▼

Yes, you can automate Fabric IQ ontology workflows using the REST API and CLI. This approach supports operational automation for ontology design, graph traversals, and data source bindings, ensuring semantic consistency across your enterprise vocabulary.