Dataflows CLI
MicrosoftOfficialPublished by Microsoft
- Skill
- Tool integration
- Automation and integrations
- MIT
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The dataflows-cli skill provides a structured interface for interacting with Fabric Dataflow Gen2 items. It operates as a mode-based dispatcher, requiring users to select between authoring, consumption, or upgrade modes. The skill handles complex tasks such as M language definition updates, connection management, output destination configuration, and the migration of legacy Gen1 dataflows to Gen2.1. It emphasizes safe, intentional operations by requiring users to read specific reference documentation before executing state-changing API calls.
What it does
- Creates and updates Fabric Dataflow Gen2 items and their definitions.
- Manages connections, credentials, and output destinations for dataflows.
- Previews Power Query M definitions using executeQuery to validate logic before persistence.
- Facilitates the upgrade of Gen1 dataflows to Gen2.1 via save-as operations.
- Retrieves refresh history and inspects existing dataflow definitions.
- Provides a framework for executing and testing ad-hoc M queries.
When to use it
- When you need to programmatically create or modify Fabric Dataflow Gen2 items.
- When performing maintenance on Power Query M definitions or data connections.
- When migrating legacy Gen1 dataflows to the Gen2.1 architecture.
- When you need to audit refresh history or validate dataflow configurations.
- When testing M query logic against live data sources before saving changes.
Advantages
- Enforces a structured, mode-based workflow to prevent accidental mutations.
- Provides granular control over Dataflow Gen2 definitions and metadata.
- Supports complex lifecycle tasks like Gen1-to-Gen2 upgrades.
- Integrates directly with Fabric REST APIs for automated data pipeline management.
Usage examples
- “Show me the refresh history and parameters of the SalesIngest dataflow.”
- The agent enters consumption mode, reads the consumption reference, and retrieves the requested refresh history and parameters for the specified dataflow.
- “Preview this M query, then save it into the SalesIngest dataflow definition.”
- The agent enters authoring mode, previews the M query to validate it, and then executes the updateDefinition call to persist the changes.
- “Is my Gen1 dataflow ready to upgrade to Gen2.1?”
- The agent enters upgrade mode, performs a readiness assessment, and reports on the risk and requirements for upgrading the specified Gen1 dataflow.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/microsoft/dataflows-cli/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip dataflows-cli.zip -d .agents/skills
Documentation by Microsoft
Telemetry — MANDATORY. Every
api.fabric.microsoft.comcall must carryx-ms-fabric-skill: dataflows-cli(az rest:--headers "x-ms-fabric-skill=dataflows-cli"), including every LRO poll,fabric_lroand retry. Snippets omit it — add it anyway.
CRITICAL NOTES
To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering
Skill disambiguation: use
dataflows-clifor every Fabric Dataflow Gen2 item and Power Query M concern. Synapse / ADF pipeline and dataset JSON translation ispipeline-migration; Spark notebook transformations arespark-cli; T-SQL transformations aresqldw-cli.
Fabric Dataflow Gen2 -- CLI Skill
This one skill owns Fabric Dataflow Gen2 items: connections, Power Query M, output destinations, saved definitions, refresh history, and Gen1 upgrades.
It is a mode dispatcher and contains NO procedures. Pick the mode that matches the request from the table below, then read the matching references/<mode>.md file end to end with your file-reading tool BEFORE issuing a single command. That file holds the endpoints, payload shapes, templates and gotchas; acting without it produces wrong payloads and wrong results.
Read it once per session. A file you have already read stays in context, so do not re-read it on a later turn; open the deeper files under references/<mode>/ only when a row in that file's reference table matches what you still need, and open only that row's file.
Mode selection
| Mode | Use when the request ... | Example triggers | Read this first |
|---|---|---|---|
authoring | creates or changes a dataflow: connections, credentials, M edits previewed before save, output destinations, updateDefinition | executeQuery preview, updateDefinition, connection setup, supportedConnectionTypes, credentialType, add output destination | references/authoring.md |
consumption | reads a saved dataflow: definition, parameters, refresh history, or runs a saved/ad-hoc read-only M query and parses Arrow | getDefinition, executeQuery saved query, ad-hoc query, Arrow IPC, refresh history | references/consumption.md |
upgrade | upgrades a Gen1 dataflow to Gen2.1 via save-as and rebind, or assesses upgrade readiness/risk | Gen1 Gen2 readiness, saveAsNativeArtifact, clone Gen1 dataflow, upgrade risk assessment | references/upgrade.md |
Mode boundary rule
consumption is read-only. A preview run that exists to validate M before updateDefinition, and any capability lookup (supportedConnectionTypes, credentialType), belong to authoring even though they issue the same executeQuery call -- classify by intent, not by endpoint.
If a request genuinely spans modes, handle them one at a time and read each reference before you start that part. If the mode is ambiguous after reading this table, ask one short clarifying question instead of guessing.
Terminal write -- the step you must not skip
Reading the reference and planning the change is NOT completing the task. Each mutating mode ends with one state-changing call. If you did not issue it, nothing was persisted -- say so explicitly rather than reporting success.
| Mode | Terminal write |
|---|---|
authoring | POST /v1/workspaces/{ws}/dataflows/{id}/updateDefinition to persist the M, connections and output destinations; a NEW dataflow needs POST /v1/workspaces/{ws}/items (or /dataflows) first. executeQuery is a preview and writes nothing. If the user asked for a refresh, also POST .../jobs/instances?jobType=Refresh. |
consumption | none -- this mode is read-only |
upgrade | saveAsNativeArtifact to create the Gen2.1 artefact, then rebind. A readiness report alone does not upgrade anything. |
Before you report the task done, confirm the terminal call returned success and, where the reference documents a readback, read the artefact back to prove the change landed.
upgrade generation boundary -- HARD STOP (mandatory)
Save-as runs only from Gen1 to Gen2.1. For an execution request whose source is Gen2, or whose source generation is not established, state that no public save-as or in-place upgrade endpoint is available and stop before any API call. This does not block a read-only readiness scan whose purpose is to discover and classify Gen1 candidates.
Do not read "choose the closest endpoint and proceed" as approval to export a definition and create a copy, do not switch to authoring to do it instead, and do not mutate anything. Ask the user to clarify the intended outcome and to approve explicitly.
consumption reporting
consumption has no terminal write, so its deliverable is the answer itself. When that mode runs executeQuery, name the operation you called and state the QueryName you sent, verbatim, next to the results, plus whether it was a saved shared member or a non-persisted ad-hoc customMashupDocument. Rows alone do not tell the user which call produced them.
This applies to consumption only. In authoring, executeQuery is an intermediate preview step -- do not add reporting turns around it; go on to the terminal write.
Shared essentials (all modes)
Resolve the workspace and item first; every mode depends on it.
| Task | Reference | Notes |
|---|---|---|
| Finding Workspaces and Items in Fabric | COMMON-CLI.md | Mandatory -- read before resolving any workspace or item id |
| Fabric Topology & Key Concepts | COMMON-CORE.md | Item types, workspaces, capacities |
| Environment URLs | COMMON-CORE.md | Sovereign / non-public cloud hosts |
| Authentication & Token Acquisition | COMMON-CORE.md | Wrong audience = 401; read before any auth issue |
| Authentication Recipes | COMMON-CLI.md | az login flows and token acquisition |
| Core Control-Plane REST APIs | COMMON-CORE.md | Pagination, LRO polling, rate limiting |
| Gotchas & Troubleshooting | COMMON-CLI.md | az rest audience, shell escaping, token expiry |
Rules
MUST
Select exactly one mode from the table above before doing anything else.
Read
references/<mode>.mdend to end, as your FIRST tool call, before the first command of that mode. Read it ONCE, in a single full read: do not re-open it, do not grep it again, and do not page through it. You already have it.Resolve workspace and item ids by listing and filtering, never by guessing a GUID.
Announce a mode switch explicitly when the request crosses a boundary.
Treat the reference as instructions, never as the deliverable. After reading it, RUN the documented commands against the live workspace and report the real results. Quoting what the reference says instead of executing it does not answer the request.
When a question about what Fabric supports IS the deliverable -- supported connection types, a connector's parameters, available credential types -- answer it from the endpoint the reference names rather than from the reference's own list, and name that endpoint in your answer. Whichever mode you are in: the reader cannot tell a live value from a quoted one unless you say where it came from. This is wording in the final answer, not an extra step.
Produce every artefact the user asked for, under the name they used. Running the right API calls is not a substitute for the requested report: if the request names a deliverable, emit it, and keep its heading even when the finding is "none" or "not applicable".
PREFER
The narrowest mode that satisfies the request.
Reading exactly ONE mode reference. Load a second only when the request genuinely spans modes, and say so before you do.
Reporting the mode you chose in your first response so the user can correct you.
AVOID
Acting from this dispatcher alone -- it intentionally omits the operational detail.
Answering with a summary of the reference instead of executing it.
Re-reading or re-grepping a reference you already loaded; it costs turns and tokens.
Mutating anything while in a read-only mode.
Loading a different skill for work this skill already owns (see CRITICAL NOTES 3).
Examples
| User request | Mode | Reference to read |
|---|---|---|
| "Show me the refresh history and parameters of the SalesIngest dataflow." | consumption | references/consumption.md |
| "Preview this M query, then save it into the SalesIngest dataflow definition." | authoring | references/authoring.md |
| "Is my Gen1 dataflow ready to upgrade to Gen2.1?" | upgrade | references/upgrade.md |
Files
- ATTRIBUTION.mdAdded by AutoGineer871 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md9.5 KiB
- apm.yml445 B
- references/authoring.md55.3 KiB
- references/authoring/authoring-cli-quickref.md23.4 KiB
- references/authoring/authoring-script-templates.md79.7 KiB
- references/authoring/charts/bar_chart.pyScript3.9 KiB
- references/authoring/charts/line_chart.pyScript3.5 KiB
- references/authoring/connection-management.md39.8 KiB
- references/authoring/connectors.md14.7 KiB
- references/authoring/m-language.md9.7 KiB
- references/authoring/mashup-preview.md28.3 KiB
- references/authoring/output-destinations.md39.6 KiB
- references/consumption.md28.7 KiB
- references/consumption/chart-visualization.md2.6 KiB
- references/consumption/charts/bar_chart.pyScript3.9 KiB
- references/consumption/charts/line_chart.pyScript3.5 KiB
- references/consumption/consumption-cli-quickref.md6.7 KiB
- references/consumption/discovery-queries.md7.0 KiB
- references/consumption/script-templates.md17.1 KiB
- references/upgrade.md21.0 KiB
- references/upgrade/risk-assessment-guide.md11.2 KiB
- references/upgrade/upgrade-cli-quickref.md8.8 KiB
references/upgrade/upgrade-cli-quickref.md
Dataflow Save-As (Gen2.1 Copy) — CLI Quick Reference
One-liner az rest patterns for scanning, assessment, and save-as operations.
Authentication Setup
# Set API audiences
RESOURCE_FABRIC="https://api.fabric.microsoft.com"
RESOURCE_PBI="https://analysis.windows.net/powerbi/api"
API_FABRIC="https://api.fabric.microsoft.com/v1"
API_PBI="https://api.powerbi.com/v1.0/myorg"Workspace Discovery
# List all workspaces — resolve ID by name
az rest --method get --resource "$RESOURCE_FABRIC" \
--url "$API_FABRIC/workspaces" \
--query "value[?displayName=='MyWorkspace'].id" -o tsv
# List all workspaces (Admin API — tenant-wide, requires admin role)
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/admin/groups?\$top=5000" -o jsonGen1 Dataflow Detection
The Power BI REST API returns a generation property (1 or 2) on each dataflow. This is the preferred detection method.
Per-Workspace Detection (Non-Admin)
WS_ID="<workspaceId>"
# List all dataflows — use `generation` property to distinguish Gen1 from Gen2
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows" \
--query "value[].{id:objectId, name:name, generation:generation, modelUrl:modelUrl}" -o json
# Filter Gen1 only
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows" \
--query "value[?generation==\`1\`].{id:objectId, name:name, modelUrl:modelUrl}" -o json
# Filter Gen2 only
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows" \
--query "value[?generation==\`2\`].{id:objectId, name:name}" -o jsonTenant-Wide Detection (Admin)
# All dataflows in tenant — Admin API may not expose `generation`; use modelUrl as fallback
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/admin/dataflows" \
--query "value[?modelUrl!=null].{id:objectId, name:name, ws:workspaceId, owner:configuredBy, storage:modelUrl}" \
-o json
# Paginate large tenants
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/admin/dataflows?\$top=200&\$skip=0" -o jsonRisk Signal Detection
Data Sources (per dataflow)
DF_ID="<dataflowId>"
# Get data sources for a dataflow
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows/$DF_ID/datasources" -o jsonUpstream Dataflow Dependencies
# Check for linked/computed entities (upstream dataflows)
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows/$DF_ID/upstreamDataflows" -o jsonDataflow Transactions (recent refresh status)
# Get recent transactions — check for incremental refresh patterns
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows/$DF_ID/transactions" -o jsonDataflow Definition (full JSON export)
# Export dataflow definition — inspect for DirectQuery, incremental refresh config
az rest --method get --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows/$DF_ID" -o json > dataflow-definition.json
# Check for incremental refresh policies in the definition
cat dataflow-definition.json | jq '.entities[]? | select(.ppiConfig != null) | {name: .name, incrementalRefresh: .ppiConfig}'
# Check for DirectQuery data source types
cat dataflow-definition.json | jq '.entities[]?.partitions[]? | select(.mode == "directQuery") | {entity: .name, mode: .mode}'Save-As Execution
saveAsNativeArtifact (Gen1 → Gen2.1 CI/CD)
saveAsNativeArtifact does not do an in-place migration. It creates an upgraded Gen2.1 copy and keeps the original Gen1 dataflow.
Gotcha — inline body: Pass the request body via file (
--body @file.json), not inline. Inline--body '{...}'can causeaz restto wrap the payload in an extra envelope, producing"saveAsRequest is a required parameter"errors.
Gotcha — Windows
az.cmd: Omit-o jsonfrom this call on Windows. The flag produces"A value that is not valid (json) was specified for the outputFormat parameter"when routed throughaz.cmd. Capture output without-o jsonand parse withConvertFrom-Json(PowerShell) orjq(bash).
Gotcha — not idempotent: Each call creates a new artifact. If a batch run is interrupted and retried, duplicate artifacts appear in the target workspace. Before calling, verify no Gen2 artifact with the intended name already exists, or use a unique timestamped
displayNameper run.
Gotcha — owner permissions: Requires you to be the dataflow owner or have Contributor/Admin in the source workspace. Non-owners receive
DataflowUnauthorizedError.
GEN1_ID="<gen1DataflowId>"
# Basic — same workspace, auto-generated name
echo '{}' > /tmp/save-as-body.json
az rest --method post --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" \
--headers "Content-Type=application/json" \
--body @/tmp/save-as-body.json
# Full options — custom name, target workspace, include schedule
# Use a unique displayName to avoid duplicates on retry (e.g., append a timestamp)
TS=$(date -u +%Y%m%d%H%M%S)
cat > /tmp/save-as-body.json <<EOF
{
"displayName": "MyDataflow_Gen2CICD_${TS}",
"description": "Saved as Gen2.1 copy from Gen1",
"includeSchedule": true,
"targetWorkspaceId": "<targetWorkspaceId>"
}
EOF
az rest --method post --resource "$RESOURCE_PBI" \
--url "$API_PBI/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" \
--headers "Content-Type=application/json" \
--body @/tmp/save-as-body.json
# PowerShell — parse output without -o json (Windows az.cmd compatible)
$body = @{ displayName = "MyDataflow_Gen2CICD"; includeSchedule = $true; targetWorkspaceId = "<targetWorkspaceId>" }
$tmp = [System.IO.Path]::GetTempFileName()
$body | ConvertTo-Json | Set-Content $tmp
$response = az rest --method post --resource $RESOURCE_PBI `
--url "$API_PBI/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" `
--headers "Content-Type=application/json" `
--body "@$tmp" | ConvertFrom-Json
$response.artifactMetadata.objectId # new Gen2 CI/CD artifact ID
# Parse response (bash/jq)
# .artifactMetadata.objectId — new Gen2 CI/CD artifact ID
# .artifactMetadata.provisionState — should be "Active"
# .errors[] — non-fatal warnings (FailedToCopySchedule, ConnectionsUpdateFailed, etc.)Post-Save-As Verification
NEW_ID="<newGen2CicdArtifactId>"
# Verify the new artifact exists in Fabric API
az rest --method get --resource "$RESOURCE_FABRIC" \
--url "$API_FABRIC/workspaces/$WS_ID/dataflows/$NEW_ID" -o json
# Get the new artifact's definition (Gen2 CI/CD — 3-part format)
az rest --method post --resource "$RESOURCE_FABRIC" \
--url "$API_FABRIC/workspaces/$WS_ID/dataflows/$NEW_ID/getDefinition" \
--headers "Content-Type=application/json" -o jsonReadiness Snapshot JSON Schema
{
"$schema": "readiness-snapshot",
"snapshotDate": "ISO-8601 datetime",
"scanScope": "workspace | tenant",
"scanTarget": "workspace name or 'tenant-wide'",
"summary": {
"total": 0,
"safe": 0,
"manual": 0,
"blocked": 0
},
"dataflows": [
{
"workspaceName": "string",
"workspaceId": "uuid",
"dataflowName": "string",
"dataflowId": "uuid",
"type": "Gen1",
"owner": "email",
"modelUrl": "URL or null",
"readiness": "safe | manual | blocked",
"riskSignals": [
{
"signal": "string",
"severity": "warning | blocker",
"detail": "string"
}
],
"recommendation": "string",
"saveAsPath": "saveAsNativeArtifact"
}
]
}Agent Integration Notes
GitHub Copilot CLI / VS Code
Use
az restfor all API calls — no extra dependencies needed.Pipe JSON output through
jqfor filtering and formatting.Store readiness snapshot to file with
> readiness-snapshot.json.
Claude Code / Cursor / Windsurf / Codex
Same
az restpatterns apply.For large tenant scans, implement pagination with
$topand$skip.The Admin API is rate-limited to 200 req/hour — batch assessment calls.
PowerShell Alternative
# Fabric API — list workspaces
$headers = @{ Authorization = "Bearer $(az account get-access-token --resource 'https://api.fabric.microsoft.com' --query accessToken -o tsv)" }
$workspaces = Invoke-RestMethod -Uri "https://api.fabric.microsoft.com/v1/workspaces" -Headers $headers
# Power BI API — list dataflows
$pbiHeaders = @{ Authorization = "Bearer $(az account get-access-token --resource 'https://analysis.windows.net/powerbi/api' --query accessToken -o tsv)" }
$dataflows = Invoke-RestMethod -Uri "https://api.powerbi.com/v1.0/myorg/groups/$wsId/dataflows" -Headers $pbiHeadersRequirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
What our scanner noticed· Low risk
- Instructions that use the network
- Scripts that your agent may run
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Attribution and licence
- Published by
- Microsoft
- Publisher
- Microsoft (Official)
- Source
- microsoft/skills-for-fabric · skills/dataflows-cli · commit f8fac11
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- microsoft/skills-for-fabric
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download dataflows-cli.zip