Power BI Project

AI Can Now Check Its Edits in Power BI

How does your AI agent check the Power BI report it just edited? Point it at the Power BI Desktop Bridge, a local server that ships on by default in the June 2026 release of Power BI Desktop, so your agent can reload its PBIR edits, check for unsaved changes, and screenshot the report to grade its own work before you ever look.

What you'll learn

  • Connect an AI agent to Power BI Desktop through the Desktop Bridge and reload its PBIR edits without closing and reopening Desktop

  • Have the agent run the manifest command first so it only calls the functions your Desktop version actually supports

  • Use status to avoid overwriting unsaved work, and screenshot so the agent can visually verify its own edits

  • Drive an edit, screenshot, and grade loop from Claude Code to add conditional formatting to a matrix, then read the agent's own iteration log

  • Write looping prompts with specific, measurable acceptance criteria the agent can grade a screenshot against

Resources

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How To Use Microsoft's Fabric Skills To Build a Power BI Report

Microsoft's official Skills for Fabric can author an entire Power BI report from one plain-English ask, the semantic model, the DAX measures, the page layout, and the publish to Fabric, and you run it yourself by pointing Claude Code at the open-source repo. This is the honest, unedited test: I hand the powerbi-authoring skills a vague "executive sales report" ask and let them run, stopping only where it teaches you something, including where the AI catches its own measure bug and the asterisk that a published report that can't refresh still isn't done.

What you'll learn

  • Point Claude Code at the open-source skills-for-fabric repo and run the powerbi-authoring plugin yourself

  • Drive a full report build from a single vague ask, with the planning skill interviewing you on scope and delivery before it writes anything

  • Read the Design Brief the AI commits to in plain YAML and approve it before a single visual exists

  • Let semantic-model-authoring write your TMDL tables, relationships, and measures into a PBIP, then review them like a fast junior analyst's work

  • Watch the report-authoring skill lay out the pages in PBIR, screenshot its own work, catch a year-over-year measure bug, and fix it on its own

  • Understand why a report published through the management skill lands empty, and what refresh and gateway work is still on you

Resources

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Microsoft Shipped Open-Source AI Skills for Fabric

At Build 2026, Microsoft shipped an official, open-source repo of AI skills for Fabric that you install and run in your own AI tools (Claude Code, Copilot CLI, Codex, Cursor, Windsurf). They're written in prose, not code, so even a non-coder can read what they do, and in this first look one of them inventories and documents an entire Fabric workspace from a single one-line prompt.

What you'll learn

  • What microsoft/skills-for-fabric actually is, and why it signals Fabric going agentic in the open

  • How one one-line prompt ("Document the Samples workspace") discovers items, reads schemas, and writes the docs

  • The mental model that ties it together: connections, skills, personas, and bundles

  • How to install a bundle in Claude Code and wire up the Fabric MCP, including filtering to a single workload

Resources

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The New Tabular Editor CLI That AI Agents Can Run

The new Tabular Editor CLI lets an AI agent operate your Power BI semantic model from the terminal, initialize a project, write C# scripts to add measures, run the Best Practice Analyzer to validate and auto-fix violations, deploy to the Service, and more, with every change landing as a reviewable git diff.

What you'll learn

  • Initialize a fresh TMDL model (or a full PBIP project) from nothing with a single te init command

  • Have an AI agent write and run a C# script to add a measure to an existing model

  • Run the Best Practice Analyzer headlessly, read the JSON findings, and auto-fix the fixable violations

  • Review every agent change as a line-level TMDL git diff before you keep it

  • Deploy the model to a Power BI workspace from the command line with te deploy and te refresh

Resources

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The Power BI Modeling MCP Server — Hands-On

How do you let an AI agent actually edit your Power BI semantic model — not just hand you DAX to paste? You connect a Copilot- or Claude-style agent to your live model through Microsoft's official Power BI Modeling MCP server and the XMLA endpoint, then drive bulk model work (descriptions, hides, sort-bys, mark-as-date, new measures, even SVG-via-DAX visuals) in a fraction of the time — with the changes landing as a reviewable git diff when the model is saved as a Power BI project.

What you'll learn

  • Connect an AI agent to your live Power BI semantic model via the Modeling MCP server and the XMLA endpoint

  • Add descriptions to every table, column, and measure in your model in one prompt

  • Run an optimization pass where the agent proposes a plan first, you approve it, and then it executes

  • Verify what the agent actually changed by reading the TMDL git diff in a Power BI project

  • Roll back any change you do not like, file by file, using source control as your undo button

  • Generate an SVG sparkline visual by asking the agent to write the DAX measure for it

Resources

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Get Your Power BI Reports AI-Ready with GitHub

A beginner-friendly setup walkthrough for getting a Power BI project into a GitHub repository so you can safely bring AI into your reporting workflow, with rollback and visible diffs as the safety net.

What you'll learn

  • Install Git and Visual Studio Code and configure Git with your name and email

  • Create a GitHub repository and clone it locally with VS Code

  • Save an existing Power BI report as a Power BI project (PBIP) inside the repo

  • Use a .gitignore to keep the local data cache out of the repository

  • Run a full lifecycle pass: branch, add a DAX measure, view the diff, commit, sync, and merge a pull request

Resources

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Power BI as Code: The Setup AI Needs for Your Reports

Save your Power BI report as a PBIP project and it lands on disk as a folder of text — JSON for the report layer, TMDL for the model — which is the format AI tools like Claude and Copilot can actually read, reason about, and edit. This video walks through the three preview features that make that possible, then tours what each piece of the project looks like on disk.

What you'll learn

  • Enable the three Power BI Desktop preview features that make PBIP usable for AI workflows (PBIP save option, TMDL semantic model, enhanced report metadata)

  • Save an existing .pbix as a PBIP project and see what changed on disk

  • Read the .Report folder — how PBIR represents your report as files and folders of JSON

  • Read the .SemanticModel folder — how TMDL captures your data model as plain text

  • Use TMDL triple-slash (///) comments as model descriptions that round-trip from AI edits back into Desktop

Resources

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