Expert guidance for the Fabric CLI (fab) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and…

GPL-3.0Auto-check passedData & Analytics

Install Fabric CLI

skills CLI
$ npx skills add data-goblin/power-bi-agentic-development --skill fabric-cli -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install data-goblin/power-bi-agentic-development fabric-cli --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/data-goblin/power-bi-agentic-development.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/fabric-cli/skills/fabric-cli .claude/skills/fabric-cli && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fabric-cli
GitHub stars
1k
Token cost
~10k tokens
SKILL.md length
4,196 words
Files
43 (incl. scripts, references)
Skills in repo
33
Repo updated
First seen
Licence
GPL-3.0

At a glance

Expert guidance for the Fabric CLI (fab) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and…

  • Works in 12 steps: Check the commands, syntax, and auth… → Check if the item exists if the user… → Find an item by name across every… → …
  • Tasks that involve REST APIs
  • SKILL.md covers When to use this skill, Critical general rules, Quickstart guide and Essential Concepts, plus 5 more sections
  • Calls az, python3 and uv

What it does

Fabric CLI is an agent skill from data-goblin/power-bi-agentic-development. Expert guidance for the Fabric CLI (fab) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, fab, or asks to create, run, refresh, schedule, deploy or delete…

Its SKILL.md is about 10k tokens, which your agent loads only when the skill is triggered. The skill folder holds 44 other files, including scripts and reference files (for example `references/admin.md`, `references/capacity-cost.md` and `references/connections.md`).

It sits in Data & Analytics, covering REST APIs, Data warehousing and Deployment. It works with Power BI. The repository describes itself as: Power BI AI skills and Power BI agents for Claude Code and GitHub Copilot: a plugin marketplace of Power BI skills, subagents, and hooks for semantic models, DAX, TMDL, reports… The licence is GPL-3.0.

When your agent uses it

  • Tasks that involve REST APIs
  • Tasks that involve Data warehousing
  • Tasks that involve Deployment

Example prompts

  • “/fabric-cli”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. Check the commands, syntax, and auth status: fab --help and fab auth status
  2. Check if the item exists if the user gave the workspace and item name: fab exists "spaceparts-dev.Workspace/spaceparts-otc-full.SemanticMod…
  3. Find an item by name across every workspace the user can see: fab find 'sales' -P type=Report -l (substring on name, description…
  4. Find the workspace: fab ls
  5. Find the item: fab ls "Workspace Name.Workspace"
  6. Check the commands for that item
  7. What's in that item; what's it for; what is it?
  8. Get files, tables, or table schemas
  9. Query data (always prefer the wrapper scripts over raw fab api / duckdb / sqlcmd; they resolve IDs, hosts, and auth for you)
  10. Set properties for an item or workspace: fab set "ws.Workspace/Item.Notebook" -q displayName -i "New Name" or fab set "ws.Workspace" -q…
  11. Review or manage permissions
  12. Deploy items to Fabric: fab import "ws.Workspace/New.Notebook" -i ./local-path/Nb.Notebook -f

What it can do on your machine

Read from SKILL.md and the folder at commit 41886f2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • az
    • python3
    • uv
    • duckdb
    • winget
    • brew

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • learn.microsoft.com
    • github.com
    • microsoft.github.io
    • dax.guide
    • powerquery.guide

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fabric CLI loads about 10k tokens when it runs, and up to ~107k if it reads all its reference files. Until then it costs about 155 tokens; SKILL.md has 4,196 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~155
When it runs · the whole SKILL.md, loaded when a task matches
~10k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~107k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from data-goblin/power-bi-agentic-development at commit 41886f2, republished under its GPL-3.0 licence (© data-goblin). 4,196 words, ~10,104 tokens.

Download SKILL.mdSave it as .claude/skills/fabric-cli/SKILL.md (or your agent's skills folder). This skill also uses 42 other files; get the full folder from GitHub.
name
fabric-cli
description
Expert guidance for the Fabric CLI (`fab`) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, `fab`, or asks to create, run, refresh, schedule, deploy or delete anything in Fabric, including preview items such as Plan, Ontology, Graph or Copilot.

Fabric CLI

Guidance for using fab to programmatically manage Fabric & Power BI service

  • Install via uv tool install ms-fabric-cli (get uv via winget install uv or brew install uv)
  • Fabric CLI is for working with the Cloud environment and not local files; it works with Power BI Pro, PPU, or Fabric; you DO NOT need a Fabric SKU to use the Fabric CLI
  • Keep fab current: check the installed version against the latest ms-fabric-cli release and upgrade with uv tool upgrade ms-fabric-cli unless the user has pinned a specific version. Discover commands and flags with fab --help and fab <command> --help rather than hard-coding behavior; the CLI surface changes regularly

[!IMPORTANT] Any time you encounter errors, user preferences or learnings when using the Fabric cli, ALWAYS note these down in the user memory rules, i.e. .claude/rules/fabric-cli.md for future improvement. This is ONLY for generic learnings and not for item- or task-specific learnings.

When to use this skill

  • Use whenever the user mentions "Fabric" or "Power BI"
  • Use when user asks about Power BI workspaces, deployment, tenants, publishing, download, permissions, or data

Critical general rules

  • IMPORTANT: The first time you use fab run check that it is up to date to the latest version (upgrade with uv tool upgrade ms-fabric-cli unless the user has pinned a version) and run fab auth status; If user isn't authenticated, ask them to run fab auth login
  • Always use fab --help and fab <command> --help the first time you use a command to understand its syntax
  • You must search the skill /references/ for relevant reference files that explain certain commands, examples, scripts, or workflows before you start using fab
  • Before first use, ask the user if they have Fabric admin access, sensitivity labels or DLP policies, any API restrictions, or preferences for Fabric/Power BI API usage; remind user to add this to memory files
  • If workspace or item name is unclear, ask the user first, then verify with fab ls or fab exists before proceeding
  • Ensure that you avoid removing or moving items, workspaces, or definitions, or changing properties without explicit user direction
  • IMPORTANT: Before any operation that consumes capacity units (creating items, running notebooks, pipelines or refreshes, large queries, Copilot, preview items such as Plan, Ontology or Graph, anything scheduled), estimate its CU-hours and its impact on the capacity, and get explicit user approval when the charge is flat or per session, exceeds a quarter of the capacity's daily CU-hours, recurs, cannot be estimated, or the capacity already throttles; a user naming a feature is not consent to its cost. See Capacity cost and capacity-cost.md
  • If a command is blocked in your permissions and you try to use it, stop and ask the user for clarification; never try to circumvent it
  • Create output directories before export: fab export does not create intermediate directories; mkdir -p the output path first or the command fails with [InvalidPath]
Capacity cost: estimate before acting

Bursting hides cost: operations finish fast and their CUs are smoothed into the next 5 to 64 minutes (interactive) or 24 hours (background). On a small F SKU one operation can use more than a day of capacity, and the carry forward then throttles every workspace on it until idle capacity pays it back, which on an F2 can take weeks.

  • Check the capacity first: fab ls .capacities -l for SKU and state; the Monitoring hub capacity page for utilization, throttling and carry forward. Any carry forward means no headroom
  • Estimate the operation in CU-hours and EUR, and as a share of the SKU's daily budget (an F<n> has 24 * n CU-hours a day; an F2 has 48)
  • Know the flat charges: Plan (preview) bills 30-day sessions per user (Planner 847, Stakeholder 168, Viewer 37 CU-hours); creating and editing a plan by REST was billed as a Stakeholder session (168 CU-h), and a session cannot be ended or refunded
  • Pausing (fab stop) bills the whole carry forward at once and clears throttling; scaling up burns it down faster at a similar total cost
  • Full thresholds, formulas, known charges and where to see what an operation cost: capacity-cost.md
Use -f (force) for non-interactive use

The fab CLI prompts for confirmation, so you you must always append -f to prevent this UNLESS sensitivity labels are enabled, in which case you must ask the user. Do this for the commands:

  • fab get -q "definition" ; sensitivity label confirmation
  • fab export ; sensitivity label confirmation
  • fab import ; overwrite confirmation
  • fab cp / fab cp -r ; overwrite and sensitivity label confirmation
  • fab rm ; delete confirmation
  • fab assign / fab unassign ; capacity/domain assignment confirmation
  • fab mv ; rename/move confirmation

Quickstart guide

You must read and understand the common list of operations with simple examples

  1. Check the commands, syntax, and auth status: fab --help and fab auth status
  2. Check if the item exists if the user gave the workspace and item name: fab exists "spaceparts-dev.Workspace/spaceparts-otc-full.SemanticModel"
  3. Find an item by name across every workspace the user can see: fab find 'sales' -P type=Report -l (substring on name, description, workspace; -P type= to filter, -l for ids; -q '<jmespath>' for client-side filter/projection). For governance workflows that need last visit / last refresh / owner / storage mode / capacity SKU, use scripts/search_across_workspaces.py; see workspaces.md for the delta.
  4. Find the workspace: fab ls
  5. Find the item: fab ls "Workspace Name.Workspace"
  6. Check the commands for that item:
    • fab desc to get itemTypes
    • fab desc .<ItemType> for commands i.e. fab desc .SemanticModel
  7. What's in that item; what's it for; what is it?:
    • Full TMDL definition: fab get "spaceparts-dev.Workspace/spaceparts-otc-full.SemanticModel" -q "definition" -f
    • Search a specific measure / table / column: fab get "ws.Workspace/Model.SemanticModel" -q "definition" -f | rga -i "Sales Amount"
    • Retrieve AI instructions / AI schema: python3 scripts/get_semantic_model_ai_metadata.py "ws.Workspace/Model.SemanticModel" --instructions-out instructions.md --schema-out schema.json
  8. Get files, tables, or table schemas:
    • List lakehouse files: fab ls "ws.Workspace/LH.Lakehouse/Files"
    • List lakehouse tables: fab ls "ws.Workspace/LH.Lakehouse/Tables"
    • Table schema: fab table schema "ws.Workspace/LH.Lakehouse/Tables/gold/orders"
  9. Query data (always prefer the wrapper scripts over raw fab api / duckdb / sqlcmd; they resolve IDs, hosts, and auth for you):
    • Semantic model (DAX): python3 scripts/execute_dax.py "ws.Workspace/Model.SemanticModel" -q "EVALUATE TOPN(10, 'Orders')"
    • Lakehouse SQL endpoint, warehouse, or SQL database (T-SQL): prefer the fabric-sql MCP execute_query(workspaceId, itemId, query) when it is loaded; fall back to python3 scripts/query_sql_endpoint.py "ws.Workspace/LH.Lakehouse" -q "SELECT TOP 10 * FROM dbo.orders". See querying-data.md
    • Lakehouse or warehouse Delta over OneLake (DuckDB): python3 scripts/query_lakehouse_duckdb.py "ws.Workspace/LH.Lakehouse" -q "SELECT * FROM tbl LIMIT 10" -t gold.orders
  10. Set properties for an item or workspace: fab set "ws.Workspace/Item.Notebook" -q displayName -i "New Name" or fab set "ws.Workspace" -q description -i "Production environment"
  11. Review or manage permissions:
    • Item ACL: fab acl ls "ws.Workspace/Model.SemanticModel" then fab acl set "ws.Workspace/Model.SemanticModel" -I user@contoso.com -R Read
    • Workspace roles: fab acl ls "ws.Workspace" then fab acl set "ws.Workspace" -I user@contoso.com -R Member
    • Setting up a service principal for automation instead of a human identity: service-principals.md - creation via az CLI, the workspace-role-plus-tenant-setting-group double requirement, and how to authenticate fab as it
  12. Deploy items to Fabric: fab import "ws.Workspace/New.Notebook" -i ./local-path/Nb.Notebook -f
  13. Download items from Fabric: fab export "ws.Workspace/Nb.Notebook" -o ./backup -f (always mkdir -p ./backup first)
  14. Copy or move items between workspaces: fab cp "dev.Workspace/Item.Notebook" "prod.Workspace" -f or fab mv "ws.Workspace/Old.Notebook" "ws.Workspace/New.Notebook" -f
  15. Open item in Fabric via browser: fab open "spaceparts-dev.SpaceParts/Amazing Report.Report"
  16. Using Fabric or Power BI APIs: fab api -A powerbi "groups/<ws-id>/datasets/<model-id>/refreshes" -X post -i '{"type":"Full"}' or fab api "workspaces/<ws-id>/items"
  17. Using Azure CLI (advanced) when Fabric CLI doesn't suffice:
    • T-SQL over any SQL-capable item ; use scripts/query_sql_endpoint.py (reuses az login via ActiveDirectoryAzCli; full walkthrough in querying-data.md)
    • Pass a Key Vault secret to a consumer without ever reading, echoing, or persisting it: az login --service-principal -u <appId> -t <tenantId> --password "$(az keyvault secret show --vault-name <vault> --name <secret> --query value -o tsv)" ; command substitution pipes the secret directly into the child process arg list, never stdout, a file, or a named shell variable
    • Full fab-vs-az decision matrix: fab-vs-az-cli.md

Essential Concepts

For information about any concepts related to Power BI or Fabric you must search or fetch via the microsoft-learn MCP server (or the pbi-search CLI as an alternative) and ask the user questions with the AskUserQuestion tool; NEVER guess or make assumptions.

Workspaces
  • Workspaces are containers for items like Notebooks (and other ETL items), Lakehouses (and other data items), SemanticModels, Reports (and other consumption items), and OrgApps.
  • Workspaces can be assigned to different things:
    • Deployment Pipelines for lifecycle management (Dev, Test, Prod, etc.)
    • Domains for governance and tenant structuring
    • Capacities for licensing and resources (Fabric or Premium capacities only; PPU and Pro work differently)
    • Git repositories for Source Control via Git integration

Key Patterns

Pay special attention to each of the following areas when using the Fabric CLI

Path Format

Fabric uses filesystem-like paths with type extensions:

"WorkspaceName.Workspace/ItemName.ItemType"

You must quote paths with spaces and punctuation:

"Workspace Name.Workspace/Semantic Model Name.SemanticModel"

For lakehouses this is extended into files and tables:

WorkspaceName.Workspace/LakehouseName.Lakehouse/Files/FileName.extension or /WorkspaceName.Workspace/LakehouseName.Lakehouse/Tables/TableName

For Fabric capacities you have to use fab ls .capacities

Examples:

  • "Production Workspace.Workspace/Sales Report.Report"
  • Data.Workspace/MainLH.Lakehouse/Files/data.csv
  • Data.Workspace/MainLH.Lakehouse/Tables/dbo/customers
Common Item Types
  • .Workspace - Workspaces
  • .SemanticModel - Power BI datasets
  • .Report - Power BI reports
  • .Notebook - Fabric notebooks
  • .DataPipeline - Data pipelines
  • .Lakehouse / .Warehouse/ .SQLDatabase - Data artifacts
  • .SparkJobDefinition - Spark jobs
  • .AISkill - Fabric Data Agents
  • .MirroredDatabase / .MirroredWarehouse - Mirrored databases
  • .Environment - Spark environments
  • .UserDataFunction - User data functions

Full list: You must use fab desc or fab desc .<ItemType> to check syntax and types if the user asks about an item type not listed above.

JMESPath Queries

Filter and transform JSON responses with -q:

bash
# Get single field
-q "id"
-q "displayName"

# Get nested field
-q "properties.sqlEndpointProperties"
-q "definition.parts[0]"

# Filter arrays
-q "value[?type=='Lakehouse']"
-q "value[?contains(name, 'prod')]"

# Get first element
-q "value[0]"
-q "definition.parts[?path=='model.tmdl'] | [0]"
Using fab api

fab has an api escape hatch that lets you use any API even if it doesn't have primary commands.

Variable Extraction Pattern

To use fab api you need item IDs. Extract them like this:

bash
WS_ID=$(fab get "ws.Workspace" -q "id" | tr -d '"')
MODEL_ID=$(fab get "ws.Workspace/Model.SemanticModel" -q "id" | tr -d '"')

# Then use in API calls
fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" -X post -i '{"type":"Full"}'
Admin APIs (Requires Admin Role)

Don't use admin commands or APIs if the user doesn't have Admin access. Here's some examples:

bash
# Find semantic models by name (cross-workspace)
fab api "admin/items" -P "type=SemanticModel" -q "itemEntities[?contains(name, 'Sales')]"

# Find all notebooks
fab api "admin/items" -P "type=Notebook" -q "itemEntities[].{name:name,workspace:workspaceId}"

# Find all lakehouses
fab api "admin/items" -P "type=Lakehouse"

# Common types: SemanticModel, Report, Notebook, Lakehouse, Warehouse, DataPipeline, Ontology

For full admin API reference (cross-workspace discovery, tenant settings read/update, capacity/domain/workspace overrides, activity events): admin.md

Error Handling & Debugging
bash
# Show response headers
fab api workspaces --show_headers

# Verbose output
fab get "Production.Workspace/Item" -v

# Save responses for debugging
fab api workspaces -o /tmp/workspaces.json

Common workflows

These are the most common workflows you'll encounter in Fabric

Finding or exploring workspaces, items, or metadata
CommandPurposeExample
fab lsList workspaces / itemsfab ls "Sales.Workspace" -l
fab existsCheck if a path existsfab exists "Sales.Workspace/Model.SemanticModel"
fab getGet item detailsfab get "Sales.Workspace" -q "id"
fab descSupported commands per typefab desc .SemanticModel

Flags:

  • -l (long listing)
  • -a (show hidden items)
  • -q (JMESPath filter)
  • -v (verbose output)
  • -o (save response to file)

Fabric discovery follows a drill-down pattern:

  • Browsing:
    • List workspaces: fab ls
    • List items in a workspace: fab ls "ws.Workspace" -l
    • Confirm a path exists: fab exists "ws.Workspace/Item"
    • Check what commands an item type supports: fab desc .<ItemType>
  • Inspection:
    • Get item details: fab get "ws.Workspace/Item"
    • Pull a single field: fab get "ws.Workspace" -q "id"
  • Cross-workspace search:

Check references before exploring:

Querying data
CommandPurposeExample
fab get -q "definition"Get model schemafab get "ws.Workspace/Model.SemanticModel" -q "definition" -f
fab api -A powerbiExecute DAXfab api -A powerbi "groups/<ws-id>/datasets/<model-id>/executeQueries" -X post -i '{"queries":[{"query":"EVALUATE..."}]}'
fab lsBrowse files / tablesfab ls "ws.Workspace/LH.Lakehouse/Files"
fab table schemaLakehouse table schemafab table schema "ws.Workspace/LH.Lakehouse/Tables/sales"
fab cpUpload / download OneLake filefab cp ./local.csv "ws.Workspace/LH.Lakehouse/Files/"
duckdb + delta_scanQuery Delta tables (requires DuckDB)duckdb -c "... delta_scan('abfss://<ws-id>@onelake.../<lh-id>/Tables/schema/table')"
duckdb + read_csv/jsonQuery raw files (requires DuckDB)duckdb -c "... read_csv('abfss://.../Files/data.csv')"

Flags:

  • -A fabric|powerbi|storage|azure (API audience)
  • -X get|post|put|delete|patch (HTTP method)
  • -i (JSON body or file)
  • -f (skip sensitivity prompt on definition pulls).

Fabric exposes three query paths depending on the source; always prefer the wrapper scripts -- they resolve IDs, hosts, and auth for you:

  • Semantic models (DAX):
    • Find model fields first: fab get "ws.Workspace/Model.SemanticModel" -q "definition"
    • Query: scripts/execute_dax.py
  • Lakehouses / Warehouses via Delta over OneLake (DuckDB):
  • Lakehouse SQL endpoint, Warehouse, or SQL Database (T-SQL):
    • Prefer the fabric-sql MCP execute_query(workspaceId, itemId, query) when loaded; server-side, no local tooling
    • Fall back to scripts/query_sql_endpoint.py (sqlcmd; auto-detects host per item type, reuses az login via ActiveDirectoryAzCli) when the MCP is unavailable
    • Prefer either over DuckDB when you need INFORMATION_SCHEMA, sys.* metadata, CTEs, or window functions
    • Full route priority: querying-data.md

Check references before writing queries:

Changing metadata or access (descriptions, tags, endorsement, properties, bindings, permissions)
CommandPurposeExample
fab setUpdate propertyfab set "ws.Workspace/Item" -q displayName -i "New Name"
fab mvRename / move itemfab mv "ws/Old.Notebook" "ws/New.Notebook" -f
fab acl lsList permissionsfab acl ls "ws.Workspace"
fab acl setGrant permissionfab acl set "ws.Workspace" -I <objectId> -R Member
fab acl rmRevoke permissionfab acl rm "ws.Workspace" -I <upn>
fab label setSet sensitivity labelfab label set "ws/Nb.Notebook" --name Confidential

Flags:

  • -q <field> + -i <value> (set a single property)
  • -I (object ID or UPN for fab acl)
  • -R Admin|Member|Contributor|Viewer (role for fab acl set)
  • -f (skip confirmation; ask user first if sensitivity labels are in play)

Metadata and access changes fall into a few groups:

  • Properties (displayName, description, sensitivity config):
    • Native update: fab set "<path>" -q <field> -i "<value>"
    • Capture current state first so you can revert: fab get -v -o /tmp/before.json
  • Endorsement, certification, and tags (no first-class fab commands):
    • Patch via fab api with item-specific endpoints
    • Tag workflow: tags.md
    • Endorsement patterns: reference.md
  • Folder placement:
    • Move items between workspace subfolders: folders.md
  • Access control and sensitivity labels:
    • Grant / revoke: fab acl set, fab acl rm
    • Set sensitivity label: fab label set
    • Verify the principal first: az ad user show
    • Never change permissions or labels without explicit user confirmation
  • Bindings:

Check references before changing metadata:

Working with workspaces
CommandPurposeExample
fab mkdirCreate workspace / itemfab mkdir "New.Workspace" -P capacityname=MyCapacity
fab assignAttach capacity / domainfab assign .capacities/cap.Capacity -W ws.Workspace -f
fab unassignDetach capacity / domainfab unassign .capacities/cap.Capacity -W ws.Workspace
fab start / fab stopResume / pause capacityfab start .capacities/cap.Capacity
fab cp -rFork workspacefab cp "dev.Workspace" "prod.Workspace" -r -f
fab rmSoft-delete (see recovery)fab rm "ws/Item.Type" -f

Flags:

  • -P key=value (creation params for fab mkdir)
  • -W (target workspace for fab assign / fab unassign)
  • -r (recursive copy/move)
  • -bpc (block on path collision for fab cp)
  • -f (skip confirmation)

Workspace-scope operations fall into a few groups:

  • Create and provision:
    • Create workspace: fab mkdir "<Name>.Workspace" -P capacityname=<cap>
    • Attach capacity or domain: fab assign .capacities/<cap>.Capacity -W <ws>.Workspace
    • Planning context, create/get/set surface, large storage format, Spark pools, OneLake defaults, Git: workspaces.md
  • Copy, fork, download:
    • Duplicate a workspace in-tenant: fab cp -r "dev.Workspace" "prod.Workspace"
    • Dry-run the source tree first: fab ls "dev.Workspace"
    • Full local snapshot (items + lakehouse files): scripts/download_workspace.py
  • Permissions:
    • Inspect / grant / revoke: fab acl ls | set | rm
    • Tenant-wide governance audit: use the audit-tenant-settings skill from the fabric-admin plugin
  • Connections and gateways (bound to, but outside, the workspace):
    • Credential types (WorkspaceIdentity, SPN, Basic), OAuth2 limits: connections.md
    • Datasource binding, credential rotation: gateways.md
  • Folders inside a workspace:

Check references before modifying workspaces:

Show full SKILL.md (1,644 more words)Show less
Executing or scheduling jobs (notebooks, notebook cells, pipelines, semantic model refresh)
CommandPurposeExample
fab job runRun synchronouslyfab job run "ws/ETL.Notebook" -P date:string=2025-01-01
fab job startRun asynchronouslyfab job start "ws/ETL.Notebook"
fab job run-listList executionsfab job run-list "ws/Nb.Notebook"
fab job run-statusCheck statusfab job run-status "ws/Nb.Notebook" --id <job-id>
fab job run-cancelCancel a jobfab job run-cancel "ws/Nb.Notebook" --id <job-id> -w
scripts/run_notebook_checked.pyRun a notebook + verify its exit value (status Completed ≠ ETL succeeded)python3 scripts/run_notebook_checked.py "ws/ETL.Notebook"
fab api -A powerbi .../refreshesTrigger semantic model refreshfab api -A powerbi "groups/<ws-id>/datasets/<model-id>/refreshes" -X post -i '{"type":"Full"}'

Flags:

  • -P key:type=value (parameters, type is string|int|bool)
  • --id (job run ID)
  • -w (wait on cancel)
  • --timeout (overall timeout for synchronous runs)
  • --polling_interval (status poll cadence)

Jobs map to different endpoints depending on item type:

  • Notebooks and pipelines:
    • Run synchronously: fab job run "ws/ETL.Notebook" -P date:string=2025-01-01
    • Run asynchronously: fab job start "ws/ETL.Notebook"
    • Check status: fab job run-status "ws/Nb.Notebook" --id <job-id>
    • List history: fab job run-list "ws/Nb.Notebook"
    • Verify the REAL outcome: a job Completed only means the process finished -- a notebook can catch its own exception and exit a failure payload while still showing Completed. Read its exit value, or use scripts/run_notebook_checked.py; details in notebooks.md
    • Python / PySpark kernels, Livy sessions, cell-level CRUD: notebooks.md
  • Semantic model refresh (not exposed as fab job):
    • Trigger: fab api -A powerbi "groups/<ws-id>/datasets/<model-id>/refreshes" -X post -i '{"type":"Full"}'
    • Check current run before starting a new one (409 if already running): fab api -A powerbi "groups/<ws-id>/datasets/<model-id>/refreshes?\$top=1"
    • Enhanced refresh, incremental policies, partition targeting: semantic-models.md
  • Dataflow refresh:
  • Scheduling:

Check references before running jobs:

Fabric admin operations (auditing, management)
CommandPurposeExample
fab api "admin/items"Cross-workspace item searchfab api "admin/items" -P "type=SemanticModel" -q "itemEntities[?contains(name,'Sales')]"
fab api "admin/workspaces"Workspace inventoryfab api "admin/workspaces"
fab api "admin/tenantsettings"Tenant settingsfab api "admin/tenantsettings"
fab api "admin/capacities"Capacity inventoryfab api "admin/capacities"
fab api -X post .../updateUpdate tenant settingfab api -X post "admin/tenantsettings/<name>/update" -i body.json

Flags:

  • -P key=value (query params, e.g. type=SemanticModel)
  • -q (JMESPath filter)
  • -X post + -i (write ops)
  • --show_headers (inspect Retry-After on 429)

Admin-scope work is gated behind the Fabric / Power BI admin role. Confirm access first with fab api "admin/capacities" 2>&1 | head -5; if it errors, stop rather than retry.

Two entry points cover most admin tasks:

  • Governance audits (tenant settings, delegated overrides, Entra SG scoping):
    • Use the audit-tenant-settings skill from the fabric-admin plugin. It owns the curated metadata baseline, the audit + change-detection script, delegated-override enumeration, and the Entra SG investigation workflow.
    • Invoke it whenever the question combines tenant posture with group membership, override scope, or drift against the baseline.
  • Raw admin APIs (cross-workspace search, activity events, artifact access, item search):
    • Patterns in admin.md
    • Rate limit: 25 write requests / minute; honor Retry-After on 429
    • Print the exact command and wait for user confirmation before any destructive admin operation

Check references before admin work:

Definitions and deployment (item definitions, deployment pipelines, git integration, cicd)
CommandPurposeExample
fab get -q "definition"Read raw definitionfab get "ws/Model.SemanticModel" -q "definition" -f
fab exportExport item to localfab export "ws/Nb.Notebook" -o ./backup -f
fab importImport item from localfab import "ws/Nb.Notebook" -i ./backup/Nb.Notebook -f
fab cpCopy between workspacesfab cp "dev/Item" "prod.Workspace" -f
fab api "deploymentPipelines"Deployment pipelines APIfab api "deploymentPipelines" -q "value[]"

Flags:

  • -o (output path for fab export)
  • -i (input path or JSON body for fab import)
  • --format (definition format for export / import)
  • -f (skip overwrite and sensitivity prompts)

[!IMPORTANT] The poll interval is by far the biggest performance lever for any definition change. Creating or updating an item definition is a long-running operation (LRO): the API returns 202 Accepted with a Retry-After: 20 header. fab import, nb create, and nb cell edit wait roughly that long between status polls, so a notebook that the server finishes in ~1s takes them 25-60s. Neither fab nor nb exposes a knob to change that interval. For notebook definition changes, strongly prefer scripts/deploy_notebook.py, which polls the LRO every ~0.3s (tunable via --poll-interval) and creates in ~1-2s or updates in place in ~1s. Auto-detects create vs update. When you must roll your own for another item type, the rule is the same: poll updateDefinition / create at ~0.3s, not the advertised 20s.

bash
python3 scripts/deploy_notebook.py "ws.Workspace/ETL.Notebook" -i ./ETL.Notebook   # create or update in place

Every Fabric item has a serializable definition. Move definitions between environments depending on scope:

  • Single item:
    • Round-trip locally: fab export then fab import (always mkdir -p the output directory first; fab export does not create intermediate directories and fails with [InvalidPath])
    • Same-tenant shortcut, no local hop: fab cp "dev/Item" "prod.Workspace"
  • Semantic model as PBIP (TMDL + blank report):
    • Export the model with fab export, create the report with pbir new report, then combine them with pbir report merge-to-thick; see import-download-deploy.md
  • Full workspace snapshot (items + lakehouse files):
  • Promotion between Dev, Test, Prod:
    • Fabric deployment pipelines API (covers all item types)
    • Power BI pipelines API (Power BI items only, but finer-grained deploy flags like allowPurgeData, allowTakeOver)
    • When to use each, selective deploy, LRO polling: deployment-pipelines.md
  • Git integration (connect workspace to repo, branch, commit, update from git):

Check references before deploying:

  • audit-tenant-settings (in the fabric-admin plugin) ; Fabric governance workflow covering tenant settings, delegated overrides (capacity / domain / workspace), and the Entra security groups those settings reference. Read-only; holds the curated metadata baseline and the audit + change-detection script.

Gotchas

  • IMPORTANT: DON'T try to use fab ls on items that aren't data items (.Lakehouse, .Warehouse, etc); use fab ls to find workspaces and items, and use fab get to look at definitions
  • ALWAYS Use the -f flag when using fab get, fab import, fab export, etc. as described above
  • ONLY fallback to fab api when a command doesn't exist
  • Definition changes feel slow but aren't: fab import / nb create / nb cell edit take 25-60s to push a notebook definition only because they poll the LRO at the server's Retry-After: 20. The work is ~1s. Use scripts/deploy_notebook.py (tight-polls at ~0.3s) for definition changes; the poll interval is the single biggest lever

References

Reference map (which references cluster together; follow the links between them, not just this list):

etl / notebooks
  notebooks.md ── run jobs, exit value, scheduling
    ├─ querying-data.md ── nb exec / Livy, DuckDB/sqlcmd, SQL-endpoint sync
    └─ lakehouses.md ── attach, table ops, OneLake shortcuts, SQL-endpoint id
  (cross-plugin) executing-spark, using-duckdb  ── etl plugin: ephemeral Spark, local Delta

data items
  lakehouses.md · warehouses.md · sql-databases.md · semantic-models.md
    └─ all feed querying-data.md (route priority) and notebooks.md (load then read)

governance / deploy
  admin.md · permissions.md · tags.md · folders.md
  import-download-deploy.md ─ deployment-pipelines.md ─ workspaces.md (git status)
  (cross-plugin) audit-tenant-settings ── fabric-admin plugin

Skill references:

  • Import, Download, and Deploy - Export / import / copy / move items, PBIP round-trips, dev-to-prod migration patterns
  • Querying Data - Query semantic models in DAX and lakehouses or warehouses in SQL with DuckDB
  • Lakehouses - Endpoints, file/table operations, OneLake paths
  • Warehouses - Create, browse, query via DuckDB, load data
  • SQL Databases - Create, browse, query via DuckDB, auto-mirroring
  • Semantic Models - TMDL, DAX, refresh, storage mode
  • Reports - Export, import, visuals, fields
  • Paginated Reports - RDL upload, export-to-file, datasources, parameters
  • Notebooks - Python/PySpark kernels, metadata, cell CRUD, Livy execution, scheduling
  • Workspaces - Create, manage, permissions
  • Permissions - Sharing and distribution, workspace roles, item permissions, apps, embed, B2B, deployment pipeline permissions, licensing and capacity SKUs
  • Deployment Pipelines - CI/CD, deploy stages, selective deploy, LRO polling
  • Dataflows - Gen1 and Gen2, refresh, publish, admin
  • Dashboards - Tiles, clone (dashboards are not reports)
  • Org Apps - Read-only API for distributed content packages
  • Scorecards - Goals, check-ins, status rules (Preview API)
  • Gateways - Datasources, credentials, dataset binding
  • Folders - Organize items into folders via API; includes best practices for structuring workspaces
  • Tags - Create, apply, and audit tenant/domain tags on items and workspaces via fab api (no native fab tag command)
  • Capacity cost - Estimate CU-hours and capacity impact before acting: SKU budgets, smoothing, throttling stages, carry forward and burndown, pause billing, known flat charges (Plan, Ontology, Copilot)
  • fab vs az CLI - When to use which; capacity, networking, Key Vault, monitoring, CMK, CI/CD
  • Admin APIs - Cross-workspace search, tenant operations, governance
  • API Reference - Capacities, domains, misc API patterns
  • Connections - Create, update, list connections programmatically; credential types (WorkspaceIdentity, SPN, Basic); OAuth2 limitations
  • Service Principals - Create an SP with az CLI, grant it workspace access, clear the tenant-setting gate, authenticate fab as it (real login vs env-token testing), rotation and teardown
  • Full Command Reference - All commands detailed

Scripts (scripts that you can execute):

  • search_across_workspaces.py ; cross-workspace governance complement to fab find (last visit, last refresh, owner, storage mode, capacity SKU, Copilot readiness); see workspaces.md for when to choose which
  • get-downstream-reports.py ; find all reports connected to a given semantic model across accessible workspaces (no admin required)
  • execute_dax.py ; execute DAX queries against semantic models; output as table, csv, or json
  • query_lakehouse_duckdb.py ; query lakehouse or warehouse Delta tables via DuckDB against OneLake (reuses az login); output as table, csv, or json
  • query_sql_endpoint.py ; query lakehouse SQL endpoint, warehouse, or SQL database via sqlcmd (reuses az login through ActiveDirectoryAzCli); output as table, csv, or json
  • create_direct_lake_model.py ; create a Direct Lake semantic model from lakehouse tables
  • download_workspace.py ; download a full workspace with all item definitions and lakehouse files
  • run_notebook_checked.py ; run a notebook and check its exit value, exiting non-zero when the notebook's own {ok:false} verdict fails despite a Completed job status (reads the exit value via the notebook job-instance beta endpoint)
  • deploy_notebook.py ; create or update a notebook definition fast (~1-2s) by tight-polling the LRO instead of the CLI's ~20s Retry-After cadence; auto-detects create vs update, --poll-interval is the performance lever. Strongly prefer this over fab import / nb for any notebook definition change
  • task_flow.py ; create, update, export or delete a workspace task flow from a JSON spec (tasks, items as <displayName>.<Type>, edges) through the internal metadata endpoints the Fabric UI uses; no public API or fab command exists for task flows

See scripts/README.md for detailed usage, arguments, and examples. Always search the scripts/ folder before writing a new helper; a script may already exist for the task.

External references (request markdown when possible):

© data-goblin, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 42 other files (scripts, references) in plugins/fabric-cli/skills/fabric-cli of data-goblin/power-bi-agentic-development.

  • SKILL.md
  • examples/pyspark-notebook.ipynb
  • examples/python-notebook.ipynb
  • references/admin.md
  • references/capacity-cost.md
  • references/connections.md
  • references/dashboards.md
  • references/dataflows.md
  • references/deployment-pipelines.md
  • references/fab-api.md
  • references/fab-vs-az-cli.md
  • references/folders.md
  • references/gateways.md
  • references/import-download-deploy.md
  • references/lakehouses.md
  • references/notebooks.md
  • references/org-apps.md
  • references/paginated-reports.md
  • references/permissions.md
  • … and 24 more

Open the folder on GitHubat commit 41886f2

Compare with similar skills

Fabric CLI next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fabric CLI compared with similar skills
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Powerbi Modelinggithub/awesome-copilot40k1 repos~1.5kAutomated safety check: PassMIT
Bi Dashboardrevfactory/harness-1001.3k—~1.8kAutomated safety check: PassApache-2.0
Debugging Signals PipelinePostHog/posthog-foss721—~2.4kAutomated safety check: NotesMIT
Backend Dev Guidelineslangfuse/langfuse35k—~1.9kAutomated safety check: PassCustom licence

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Works with

Questions about Fabric CLI

What does Fabric CLI do?

Expert guidance for the Fabric CLI (fab) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and…. Fabric CLI is an agent skill from data-goblin/power-bi-agentic-development. Expert guidance for the Fabric CLI (fab) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin.

When should I use Fabric CLI?

Fabric CLI fits situations like: tasks that involve REST APIs; tasks that involve Data warehousing; tasks that involve Deployment.

How do I install Fabric CLI in Claude Code?

Run `npx skills add data-goblin/power-bi-agentic-development --skill fabric-cli -a claude-code`. Or copy the skill folder (plugins/fabric-cli/skills/fabric-cli in data-goblin/power-bi-agentic-development) into .claude/skills/fabric-cli in your project. Claude Code loads it when a task matches its description.

How do I install Fabric CLI in Codex?

Run `npx skills add data-goblin/power-bi-agentic-development --skill fabric-cli -a codex`. Or copy the skill folder (plugins/fabric-cli/skills/fabric-cli in data-goblin/power-bi-agentic-development) into .agents/skills/fabric-cli in your project. Codex loads it when a task matches its description.

Can I use Fabric CLI in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add data-goblin/power-bi-agentic-development --skill fabric-cli -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fabric-cli, .gemini/skills/fabric-cli, .github/skills/fabric-cli and .opencode/skills/fabric-cli in your project.

What does Fabric CLI need to run?

Going by SKILL.md and its folder, Fabric CLI needs the command-line tools its instructions call (az, python3, uv, duckdb, winget and brew). Our summary lists: Python 3.

Does Fabric CLI access the network?

SKILL.md names 5 domains. As links in the text: learn.microsoft.com, github.com, microsoft.github.io, dax.guide and powerquery.guide. This is read from the text; nothing was executed.

Is Fabric CLI safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Fabric CLI use?

Fabric CLI is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fabric CLI use?

About 10k tokens (SKILL.md is roughly 40k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 97k tokens, read only when the agent opens those files.

What are the alternatives to Fabric CLI?

Skills that share tags, products or a category with Fabric CLI: Power Bi AI Readiness (DKH-DK/Self-Service-Power-BI-Fabric, 113 stars), Powerbi Modeling (github/awesome-copilot, 40k stars), Bi Dashboard (revfactory/harness-100, 1.3k stars) and Debugging Signals Pipeline (PostHog/posthog-foss, 721 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fabric CLI?

data-goblin (a GitHub user) maintains it in data-goblin/power-bi-agentic-development, which has 1,026 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 5, 2026.

Source: data-goblin/power-bi-agentic-development on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.