agentsclimarketplace

Fabric data agent

Skill PatrickGallucci/fabric-skills/skills/fabric-data-agent

Copilot, Claude AI Skills for Microsoft Fabric

Install
npx -y skills add PatrickGallucci/fabric-skills --skill fabric-data-agent

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 16 stars16 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

The file declares its own license as Complete terms in LICENSE.txt. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

6.6 KB, as published. Nobody here has run it

Microsoft Fabric Data Agents

Build conversational AI experiences that let users ask questions in plain English against structured data in Microsoft Fabric. Fabric Data Agents translate natural language into SQL, DAX, or KQL queries, execute them securely under the caller's identity, and return data-driven answers.

When to Use This Skill

  • Creating a new Fabric Data Agent from the portal or via REST API
  • Configuring data sources (Lakehouse, Warehouse, Power BI Semantic Model, KQL Database, Ontology)
  • Writing effective agent-level or data-source-level instructions
  • Authoring example queries (few-shot examples) to improve NL2SQL/NL2DAX/NL2KQL accuracy
  • Automating data agent provisioning with PowerShell and the Fabric REST API
  • Integrating a published Fabric Data Agent with Azure AI Foundry agents
  • Managing the Operations Agent definition (Configurations.json) programmatically
  • Publishing, sharing, and versioning data agents
  • remediate query generation, data source permissions, or tenant settings

Prerequisites

RequirementDetails
Fabric capacityPaid F2+ SKU, or Power BI Premium P1+ with Fabric enabled
Tenant settingsFabric data agent, Cross-geo processing for AI, Cross-geo storing for AI all enabled
XMLA endpointsEnabled if using Power BI Semantic Model data sources
Data source accessAt least Read permission on target lakehouses, warehouses, semantic models, or KQL databases
PowerShell (automation)PowerShell 7.4+, Az.Accounts module
Azure AI Foundry (integration)Foundry Project endpoint, Fabric connection, model deployment

Step-by-Step Workflows

Workflow 1: Create and Configure a Data Agent (Portal)

  1. Navigate to your workspace and select + New Item > Fabric data agent
  2. Provide a descriptive name for the agent
  3. Add up to 5 data sources from the OneLake catalog (any mix of Lakehouse, Warehouse, PBI Semantic Model, KQL DB, Ontology)
  4. Select/deselect tables in the Explorer pane to control what the AI can query
  5. Write agent instructions — see instruction-best-practices.md
  6. Add example queries for each data source — see example-query-guide.md
  7. Test the agent in the chat pane with representative questions
  8. Select Publish and provide a description
  9. Share the published version with colleagues via workspace permissions

Workflow 2: Automate Data Agent via REST API (PowerShell)

  1. Authenticate with Connect-AzAccount and obtain an access token
  2. Create the agent item using the Fabric Items API — run New-FabricDataAgent.ps1
  3. Configure the Operations Agent definition (Configurations.json) — see operations-agent-schema.md
  4. Update the item definition using the Update Item Definition API
  5. Validate deployment by listing items in the workspace

Workflow 3: Integrate with Azure AI Foundry

  1. Create and publish the Fabric Data Agent in the Fabric portal
  2. Create a Foundry Agent in the Azure AI Foundry portal
  3. Add a Microsoft Fabric connection to your Foundry project
  4. Create the agent with the Fabric tool enabled — see foundry-integration.md
  5. Create a thread, add a user question, run the thread, and retrieve the response
  6. Update Foundry agent instructions to describe what data the Fabric tool can access

remediate

SymptomCauseResolution
Data agent option not visible in New ItemTenant setting disabledAdmin enables "Fabric data agent" in Tenant Settings
Agent can't see my tablesTables not selected in ExplorerCheck table checkboxes in the data source Explorer
Queries return permission errorsInsufficient data accessGrant at least Read on the underlying data source
Example queries show validation errorsSQL/KQL syntax invalid or schema mismatchValidate queries against the actual table schema
Agent misinterprets domain termsMissing definitions in instructionsAdd term definitions in Agent Instructions (up to 15,000 chars)
Power BI semantic model won't addXMLA endpoints disabledEnable "Power BI semantic models via XMLA endpoints" tenant switch
Published agent not accessible to colleaguesWorkspace permissions not grantedShare the workspace or agent item with appropriate roles

References

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.