Agent skills for Fabric CLI
As I've been working on efficiently using the Fabric CLI with coding agents (GitHub Copilot, Codex), I've come up with the following repo. In this repo, I created skills that you can install locally, and your coding agent can pick them up depending on the context. For example, if you want to get details about a recent failed pipeline, you can ask your agent: "What was the cause of my pipeline 'bronze_load' in the playground workspace?" It will automatically pick up the fab-job-ops skill and dig into the root cause of the pipeline's failure. Under the hood, it uses the Fabric CLI. For installing all necessary dependencies, see the README of the repo. The simplest way is to clone the repo and install the skills first. After that, use the fab-bootstrap skill to install the Fabric CLI and authenticate against Fabric. From there on, it’s only up to your imagination what you want to create. For example, you could say: "Create a logistics workspace attached to my capacity and generate dummy data. Create bronze, silver, and gold schema." There are also conventions baked in, such as naming conventions for folders, items, columns, and more. Additionally, when modeling a semantic model, several common best practices are already included. GitHub repository: dc-floriangaerner/fab-cli-skillsFabric CLI: Command Your Data Platform Like a Pro
Introduction There's a moment every data engineer knows well: you've got a dozen Fabric workspaces to manage, pipelines to trigger, files to move into OneLake, and deployments to ship — all before standup. Clicking through the Fabric portal is fine for exploration, but when speed, repeatability, and automation matter, you need something sharper. Meet Fabric CLI (fab) — Microsoft's official open-source command-line interface for Microsoft Fabric. It brings your entire data platform to the terminal, letting you navigate workspaces, run pipelines, manage items, upload data, and wire everything into CI/CD pipelines — all without touching a browser. This post covers everything you need to get up and running: what Fabric CLI is, why it matters, how to install it, and real command examples you can use today. What Is the Fabric CLI? Fabric CLI (fab) is a cross-platform, open-source command-line tool built by Microsoft that gives you direct access to Microsoft Fabric from your terminal. Think of it as a shell that speaks fluent Fabric — it exposes workspaces, lakehouses, pipelines, semantic models, notebooks, and more as a navigable, scriptable file system. Released as Generally Available (v1.5+), it is fully supported for production use and backed by Microsoft's SLA. It works on: Windows Terminal macOS Terminal Linux shells GitHub Actions, Azure DevOps Pipelines, and any CI/CD environment At its core, fab does two things brilliantly: it mirrors familiar shell UX (think ls, cd, cp, rm) applied to Fabric resources, and it exposes automation-ready commands for running and deploying Fabric items at scale. Why Does Fabric CLI Matter? 1. Speed and Efficiency Portal navigation is built for discovery. CLI is built for execution. Once you know what you want to do, fab gets you there in a single command. 2. Automation-First Design Every fab command works identically in your local terminal and in a CI/CD YAML pipeline. There's no "portal-only" escape hatch — everything is scriptable. 3. DevOps Integration Fabric CLI bridges the gap between data engineering and modern DevOps practices. You can trigger pipelines, promote deployments, and manage Git-backed workspaces directly inside your GitHub Actions or Azure DevOps workflows. 4. Open Source and Extensible The CLI is open-source on GitHub (microsoft/fabric-cli), meaning the community can contribute, audit, and extend it. 5. Broad Item Coverage Fabric CLI supports a wide range of Fabric item types: Lakehouses, Notebooks, Data Pipelines, Semantic Models, Warehouses, Dataflows, GraphQL APIs, CosmosDB Databases, SQL Databases, Variable Libraries, Copy Jobs, Power BI Reports, and more. Installation Fabric CLI is distributed via PyPI and requires Python 3.8+. pip install ms-fabric-cli Verify your installation: fab --version You should see output like: fab version 1.5.x Tip: If you're using a virtual environment, activate it before installing to keep dependencies clean. Authentication Before running any commands, you need to authenticate. Fabric CLI supports three authentication modes. Interactive Login (Developer / Local) fab auth login This opens a browser window for Microsoft Entra ID (Azure AD) sign-in — perfect for day-to-day local use. Service Principal (CI/CD / Automation) fab auth login \ -u $CLIENT_ID \ -p $CLIENT_SECRET \ --tenant $TENANT_ID Use this in GitHub Actions or Azure DevOps secrets for unattended runs. Managed Identity (Azure-Hosted Runners) When running on an Azure-hosted machine with a managed identity, authentication is handled automatically — no credentials to manage. Navigating Your Fabric Environment Fabric CLI models your Fabric tenant as a navigable file system. If you've used a Unix shell, this will feel immediately natural. List All Workspaces fab ls Output: Sales Analytics.Workspace Marketing Data.Workspace Finance Reporting.Workspace DevOps Sandbox.Workspace Explore a Workspace fab ls "Sales Analytics.Workspace" Output: SalesLakehouse.Lakehouse DailyIngest.DataPipeline SalesModel.SemanticModel SalesReport.Report Detailed Listing fab ls -l "Sales Analytics.Workspace" Type Name Modified ----------------- ----------------------- ------------------- Lakehouse SalesLakehouse 2026-04-28 09:12 DataPipeline DailyIngest 2026-05-01 14:45 SemanticModel SalesModel 2026-05-02 08:30 Report SalesReport 2026-05-02 08:35 Change Working Context fab cd "Sales Analytics.Workspace" fab ls Once you cd into a workspace, all relative paths are scoped to it. Running Data Pipelines One of the most powerful everyday uses of Fabric CLI is triggering and monitoring Data Pipelines. Run a Pipeline fab run "Sales Analytics.Workspace/DailyIngest.DataPipeline" Output: Pipeline run started: run_id=abc123 Status: Running... Status: Succeeded ✓ (elapsed: 4m 32s) Run with Input Parameters fab run "Sales Analytics.Workspace/DailyIngest.DataPipeline" \ -i '{"param_date": "2026-05-10", "env": "production"}' Schedule a Pipeline Run fab job run-sch DailyIngest.datapipeline \ --type daily \ --interval "06:00" Working with Files and OneLake Fabric CLI makes it trivial to move data between your local machine and OneLake storage inside a Lakehouse. Upload a Local File to OneLake fab cp ./data/sales_may.csv \ "Sales Analytics.Workspace/SalesLakehouse.Lakehouse/Files/sales_may.csv" Download from OneLake to Local fab cp \ "Sales Analytics.Workspace/SalesLakehouse.Lakehouse/Files/sales_may.csv" \ ./local/downloads/ Sync an Entire Local Folder fab cp ./reports/ \ "Sales Analytics.Workspace/SalesLakehouse.Lakehouse/Files/reports/" \ --recursive List Files Inside a Lakehouse fab ls "Sales Analytics.Workspace/SalesLakehouse.Lakehouse/Files/" Managing Workspaces and Items Create a New Workspace fab mkdir "New Project.Workspace" Delete an Item fab rm "Sales Analytics.Workspace/OldPipeline.DataPipeline" Copy an Item Between Workspaces fab cp \ "Sales Analytics.Workspace/SalesReport.Report" \ "Finance Reporting.Workspace/SalesReport.Report" Deployment with fab deploy Introduced in v1.5, the deploy command enables one-command deployments across environments — perfect for promoting from dev to staging to production. Simple Deployment fab deploy \ --source "DevOps Sandbox.Workspace" \ --target "Finance Reporting.Workspace" Deploy with a Config File You can define your deployment rules in a deploy.yml: # deploy.yml source: DevOps Sandbox.Workspace target: Finance Reporting.Workspace items: - DailyIngest.DataPipeline - SalesModel.SemanticModel - SalesReport.Report Then run: fab deploy --config deploy.yml This is especially powerful inside CI/CD pipelines — the same command works locally and in automation. Power BI Scenarios (New in v1.5) Fabric CLI v1.5 extended first-class support for Power BI operations. Rebind a Report to a Different Semantic Model fab rebind \ "Finance Reporting.Workspace/SalesReport.Report" \ --model "Finance Reporting.Workspace/FinanceModel.SemanticModel" Refresh a Semantic Model fab refresh \ "Sales Analytics.Workspace/SalesModel.SemanticModel" Update Report Properties fab set \ "Sales Analytics.Workspace/SalesReport.Report" \ --property "description" \ --value "Updated May 2026 sales metrics" CI/CD Integration Where Fabric CLI truly shines is in automated pipelines. Here's a complete GitHub Actions workflow that authenticates, runs a pipeline, and deploys on merge to main. # .github/workflows/fabric-deploy.yml name: Fabric Deploy on: push: branches: - main jobs: deploy-fabric: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v4 - name: Install Fabric CLI run: pip install ms-fabric-cli - name: Authenticate with Service Principal run: | fab auth login \ -u ${{ secrets.FABRIC_CLIENT_ID }} \ -p ${{ secrets.FABRIC_CLIENT_SECRET }} \ --tenant ${{ secrets.FABRIC_TENANT_ID }} - name: Run data ingest pipeline run: | fab run "Sales Analytics.Workspace/DailyIngest.DataPipeline" - name: Deploy to Production workspace run: | fab deploy --config deploy.yml And the equivalent in Azure DevOps Pipelines: # azure-pipelines.yml trigger: branches: include: - main pool: vmImage: ubuntu-latest steps: - script: pip install ms-fabric-cli displayName: Install Fabric CLI - script: | fab auth login \ -u $(FABRIC_CLIENT_ID) \ -p $(FABRIC_CLIENT_SECRET) \ --tenant $(FABRIC_TENANT_ID) displayName: Authenticate - script: | fab run "Sales Analytics.Workspace/DailyIngest.DataPipeline" displayName: Run Pipeline - script: | fab deploy --config deploy.yml displayName: Deploy to Production Configuration and Debugging Enable Debug Logging When something goes wrong, verbose output is your best friend: fab config set debug_enabled true Now all CLI commands will output detailed request/response information. Set a Default Capacity If you're always working against the same capacity, set it once: fab config set default_capacity "My Production Capacity" Dry-Run Mode Preview what a command will do before executing it: fab run "Sales Analytics.Workspace/DailyIngest.DataPipeline" --dry-run This is invaluable for validating automation scripts before they touch production. Check Current Auth Status fab auth status When to Use Fabric CLI vs. the Portal The portal and CLI are complementary. Start in the portal when exploring; switch to the CLI when you need speed, repeatability, or automation. Getting Started Checklist pip install ms-fabric-cli — install the CLI fab auth login — authenticate with your Microsoft account fab ls — explore your workspaces fab ls -l "Your Workspace.Workspace" — inspect a workspace in detail fab run "Your Workspace.Workspace/YourPipeline.DataPipeline" — trigger your first pipeline Store credentials as secrets and add fab to your CI/CD pipeline Resources Microsoft Fabric CLI Docs GitHub: microsoft/fabric-cli Fabric CLI on PyPI Fabric CLI — Generally Available Blog Post Fabric CLI v1.5 Release Notes Fabric CLI in Azure DevOps Microsoft Learn: Fabric CLI Reference
