transform
Use this to author and change a dbt project: bootstrap a new dbt project in a repo that has none (`transform init`), write or refactor dbt model SQL from staging to marts, add tests and docs in schema.yml, manage dependencies, and define or update the semantic layer (dbt semantic models / MetricFlow: entities, dimensions, measures, metrics). Trigger it for requests like "set up a dbt project in this repo", "build a staging model for this table", "refactor this model", "add tests to this model", "create a mart for X", "define a revenue metric", or "add a dimension to this entity". Every change is a reviewable diff to the dbt project; any warehouse build is dev-target only, gated, and cost-surfaced first. Do not use it to explore or profile a warehouse (use explore) or to detect drift and reconcile a project that has fallen out of sync (use maintain).
Kubit
Bring Kubit into your AI workflow — query your warehouse with natural language
inventory_management
Database schema and business logic for inventory tracking including products, warehouses, and stock levels.
answering-natural-language-questions-with-dbt
Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.
fabric-data-agent
Create, configure, and manage Microsoft Fabric Data Agents that enable natural language Q&A over lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies. Use when asked to build data agents, configure NL2SQL/NL2DAX/NL2KQL experiences, write agent instructions, create example queries, automate data agent provisioning via REST API or PowerShell, integrate Fabric data agents with Azure AI Foundry, or troubleshoot data agent configuration issues.