fabric-cli-core

작성자: microsoft

Microsoft Fabric CLI(fab)를 사용하여 작업 영역, 의미 체계 모델, 보고서, 노트북 및 Fabric 리소스를 관리합니다. 사용자가 fab, Fabric CLI 등을 언급할 때 활성화됩니다.

npx skills add https://github.com/microsoft/fabric-cli --skill fabric-cli-core

Fabric CLI Core

This skill defines safe, consistent defaults for an AI agent helping users operate Microsoft Fabric via the Fabric CLI (fab).

1 - Fabric CLI mental model (paths and entities)

Automation Scripts

Ready-to-use Python scripts for core CLI tasks. Run any script with --help for full options.

ScriptPurposeUsage
health_check.pyVerify CLI installation, auth status, and connectivitypython scripts/health_check.py [--workspace WS]

Scripts are located in the scripts/ folder of this skill.

Paths and Entities

  • Treat Fabric as a filesystem-like hierarchy with consistent dot (.) entity suffixes in paths (e.g., .Workspace, .Folder, .SemanticModel).
  • The hierarchy structure is:
    • Tenant: The top-level container for everything.
    • Workspace: Personal or team workspace holding folders, items, and workspace-level elements.
    • Folder: Container for organizing items within a workspace (supports ~10 levels of nesting).
    • Item: Individual resource within a workspace or folder (e.g., Notebook, SemanticModel, Lakehouse).
    • OneLakeItem: OneLake storage item residing within a Lakehouse (tables, files, etc.).
  • Prefer and generate paths like:
    • /Workspace1.Workspace/Notebook1.Notebook
    • /Workspace1.Workspace/FolderA.Folder/SemanticModel1.SemanticModel
    • /Workspace1.Workspace/FolderA.Folder/lh1.Lakehouse/Tables (OneLakeItem)
  • When a user provides an ambiguous identifier, ask for the full path (or infer with stated assumptions).

2 - Modes (interactive vs command line)

  • Be explicit about which mode a user is in:
    • Interactive mode behaves like a REPL and runs commands without the fab prefix.
    • Command line mode runs one command per invocation and is best for scripts/automation.
  • The selected mode is preserved between sessions. If a user exits and logs back in, the CLI resumes in the same mode last used.
  • When you provide instructions, show commands in command line mode unless the user says they're in interactive mode.

3 - Authentication (public-safe guidance)

  • Prefer these auth patterns and do not invent new flows:
    1. Interactive user: fab auth login (browser/WAM where supported).
    2. Service principal (secret/cert): use environment variables / secure mechanisms; avoid embedding secrets in files.
    3. Service principal (federated credential): use the federated token environment variable (FAB_SPN_FEDERATED_TOKEN) and do not persist the raw token.
    4. Managed identity: supported for Azure-hosted workloads; no credentials required.
  • Never ask users to paste secrets into chat or print them back.

4 - Sensitive data handling (strict)

  • Never log or output tokens, passwords, client secrets, or raw federated tokens.
  • Validate all user inputs that could affect security:
    • Paths: Sanitize file paths and API parameters.
    • GUIDs: Validate resource identifiers before use.
    • JSON: Validate JSON inputs for proper format.
  • If a user shares sensitive strings, advise rotating/regenerating them and moving to secure storage.

5 - Hidden entities and discovery

  • Hidden entities are special resources not normally visible, following a dot-prefixed naming convention (similar to UNIX hidden files).
  • Tenant-level hidden entities (accessed from root):
    • .capacitiesfab ls .capacities / fab get .capacities/<name>.Capacity
    • .gatewaysfab ls .gateways / fab get .gateways/<name>.Gateway
    • .connectionsfab ls .connections / fab get .connections/<name>.Connection
    • .domainsfab ls .domains / fab get .domains/<name>.Domain
  • Workspace-level hidden entities (accessed within a workspace):
    • .managedidentitiesfab ls ws1.Workspace/.managedidentities
    • .managedprivateendpointsfab ls ws1.Workspace/.managedprivateendpoints
    • .externaldatasharesfab ls ws1.Workspace/.externaldatashares
    • .sparkpoolsfab ls ws1.Workspace/.sparkpools
  • To show hidden resources, recommend ls -a / ls --all.

6 - Errors and troubleshooting guidance

  • When describing failures, include:
    • What the command was trying to do
    • The likely cause
    • The next actionable step
  • If the CLI surfaces an error code/message, keep it intact and do not paraphrase away the key identifiers. (Fabric CLI emphasizes stable error codes/messages.)
  • Include request IDs for API errors to aid debugging when available.

7 - Output conventions for the agent

  • Default to concise, runnable steps.
  • When recommending commands, include:
    • Preconditions (auth, correct workspace/path)
    • Expected result
    • How to verify (e.g., follow-up fab ls / fab get)

8 - Safety defaults

  • Ask before suggesting commands that delete, overwrite, or change access/permissions.
  • If the user explicitly confirms, proceed with a clear rollback note when possible.

9 - Platform and troubleshooting reference

  • Supported platforms: Windows, Linux, macOS.
  • Supported shells: zsh, bash, PowerShell, cmd (Windows command prompt).
  • Python versions: 3.10, 3.11, 3.12, 3.13.
  • CLI file storage (useful for troubleshooting):
    • Config files are stored in ~/.config/fab/:
      • cache.bin — encrypted auth token cache
      • config.json — non-sensitive CLI settings
      • auth.json — non-sensitive auth info
      • context-<session_id> — path context for command-line mode sessions
    • Debug logs are written to:
      • Windows: %AppData%/fabcli_debug.log
      • macOS: ~/Library/Logs/fabcli_debug.log
      • Linux: ~/.local/state/fabcli_debug.log

10 - Critical operational rules

  • First run: Always run fab auth status to verify authentication before executing commands. If not authenticated, ask the user to run fab auth login.
  • Learn before executing: Always use fab --help and fab <command> --help the first time you use a command to understand its syntax.
  • Start simple: Try the basic fab command alone first before piping or chaining.
  • Non-interactive mode: Use fab in command-line mode when working with coding agents. Interactive mode doesn't work with automation.
  • Force flag: Use -f when executing commands if the flag is available to run non-interactively (skips confirmation prompts).
  • Verify before acting: If workspace or item name is unclear, ask the user first, then verify with fab ls or fab exists before proceeding.
  • Permission errors: If a command is blocked by permissions, stop and ask the user for clarification; never try to circumvent it.

11 - Common item types

ExtensionDescription
.WorkspaceWorkspace container
.FolderFolder within workspace
.SemanticModelPower BI dataset/semantic model
.ReportPower BI report
.DashboardPower BI dashboard
.NotebookFabric notebook
.LakehouseLakehouse
.WarehouseData warehouse
.DataPipelineData pipeline
.SparkJobDefinitionSpark job definition
.EventstreamReal-time event stream
.KQLDatabaseKQL database
.MLModelML model
.MLExperimentML experiment
.CapacityFabric capacity (hidden)
.GatewayData gateway (hidden)
.ConnectionConnection (hidden)

Use fab desc .<ItemType> to explore any item type.

12 - Command references

For detailed command syntax and working examples, see:

microsoft의 다른 스킬

oss-growth
microsoft
OSS 성장 해커 페르소나
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK(agent-framework-azure-ai)를 사용하여 Azure AI Foundry 에이전트를 구축합니다. AzureAIAgentsProvider로 지속적 에이전트를 만들 때, 호스팅 도구(코드 인터프리터, 파일 검색, 웹 검색)를 사용할 때, MCP 서버를 통합할 때, 대화 스레드를 관리할 때, 또는 스트리밍 응답을 구현할 때 사용합니다. 함수 도구, 구조화된 출력, 다중 도구 에이전트를 다룹니다.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Azure Application Insights로 웹앱을 계측하기 위한 지침입니다. 원격 분석 패턴, SDK 설정, 구성 참조를 제공합니다. WHEN: 앱 계측 방법, App Insights SDK, 원격 분석 패턴, App Insights란 무엇인가, Application Insights 지침, 계측 예시, APM 모범 사례.
devops
applicationinsights-web-ts
microsoft
브라우저/웹 앱을 Application Insights JavaScript SDK(@microsoft/applicationinsights-web)로 계측합니다. Real User Monitoring(RUM) — 페이지 뷰, 클릭, AJAX/fetch 종속성, 예외, 사용자 지정 이벤트, 백엔드 OpenTelemetry 트레이스와 상관관계가 있는 브라우저 측 GenAI 에이전트 트레이스에 사용합니다. SDK Loader Script 및 npm 설정, 프레임워크 확장(React, React Native, Angular), Click Analytics, 텔레메트리 이니셜라이저, 브라우저에서 생성된 에이전트/도구/모델 스팬에 대한 OTel GenAI 의미론적 규칙을 다룹니다.
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Java로 이상 탐지 애플리케이션을 구축하세요. 단변량/다변량 이상 탐지, 시계열 분석 또는 AI 기반 모니터링을 구현할 때 사용하세요.
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDK를 사용하여 대화형 언어 이해(CLU)를 구현합니다. ConversationAnalysisClient로 대화 의도와 엔터티를 분석하거나, NLP 기능을 구축하거나, 애플리케이션에 언어 이해를 통합할 때 사용합니다.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. ML 작업 영역, 작업, 모델, 데이터 세트, 컴퓨팅 및 파이프라인에 사용합니다. 트리거: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development