What problem does it solve? Authoring Microsoft Fabric IQ Ontology (preview) items requires hand-crafting a tree of base64-encoded JSON definition parts and driving long-running REST operations, which is error-prone and poorly documented. This Skill guides an AI assistant to correctly create ontologies, define entity types and properties, bind them to lakehouse or Eventhouse tables, and add relationship types from the CLI. ## Core Features & Use Cases - Ontology Item Authoring: Create Ontology items and add entity types, properties (including timeseries), and relationship types using the Fabric Create Item / Update Item Definition REST APIs with InlineBase64 parts. - Data Bindings: Bind entity types to OneLake lakehouse tables (static and timeseries) or Eventhouse/KQL database tables (timeseries only), with correct value-type mapping and key constraints. - Safe Update Workflow: Enforces a fetch-mutate-send pattern with LRO polling via x-ms-operation-id, ID persistence, and preview-and-confirm steps before any write. - Use Case: Ask the assistant to create a fleet ontology with Site and Equipment entity types, bind them to lakehouse tables, add an Eventhouse telemetry timeseries binding, and relate Equipment to Site — all scripted from the CLI. ## Quick Start Ask the assistant to create a Fabric ontology item in your workspace with an entity type bound to a specific lakehouse table.