Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding.

• GitHub Copilot CLI / VS Code: invoke the check-updates skill.
• Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.
• Skip if the check was already performed earlier in this session.

CRITICAL NOTES

1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
2. 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

GENERATION BOUNDARY -- HARD STOP (mandatory) This skill executes save-as 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 API calls. This does not block a read-only readiness scan whose purpose is to discover and classify Gen1 candidates. Do not interpret "choose the closest endpoint and proceed" as approval to export a definition and create a copy, do not invoke the authoring skill automatically, and do not mutate anything. Ask the user to clarify the intended outcome and explicitly approve any separately documented alternative.

dataflows-save-as-authoring-cli — Dataflow Save-As Gen1 → Gen2.1 CI/CD via CLI

A save-as companion for creating upgraded Gen2.1 copies from Power BI Gen1 dataflows using readiness assessment and guarded execution.

We currently cannot perform an in-place migration of your dataflow. We can use save-as to create an upgraded Gen2.1 copy while preserving the original Gen1 dataflow.

Table of Contents

Tool Stack

Agent check — verify before first operation:

az --version 2>/dev/null || echo "INSTALL: https://aka.ms/install-azure-cli"
jq --version 2>/dev/null || echo "INSTALL: apt-get install jq OR brew install jq"

Copy

Authentication & API Audiences

This skill uses two distinct API audiences. Using the wrong audience returns 401.

# Fabric Items API — list Gen2 dataflows in a workspace
az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$WS_ID/dataflows"

# Power BI REST API — list all dataflows (Gen1 + Gen2) in a workspace
az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows"

# Power BI Admin API — list all dataflows tenant-wide (requires admin role)
az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/admin/dataflows"

Phase 1 — Awareness & Readiness

Goal: "Should I use save-as, and what will happen when I create a Gen2.1 copy?"

Agentic Workflow: Discover → Assess → Classify → Report

Follow this sequence for every save-as assessment:

1. Discover — Scan workspace(s) to inventory all dataflows, identifying Gen1 vs Gen2
2. Assess — For each Gen1 dataflow, check seven risk signals
3. Classify — Assign a readiness category: ✅ Safe / ⚠️ Manual followups / ❌ Blocked
4. Report — Output a Save-As Readiness Snapshot (markdown table + JSON)

Step 1: Discover — Identify Gen1 Dataflows

The Power BI REST API returns a generation property (value 1 or 2) on each dataflow. This is the preferred detection method — a single API call per workspace.

Non-Admin Path (per workspace)

WS_ID="<workspaceId>"
RESOURCE_PBI="https://analysis.windows.net/powerbi/api"

# List all dataflows — the `generation` property distinguishes Gen1 from Gen2
ALL_DATAFLOWS=$(az rest --method get \
  --resource "$RESOURCE_PBI" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows" \
  --query "value[].{id:objectId, name:name, generation:generation, modelUrl:modelUrl, configuredBy:configuredBy}" -o json)

# Filter Gen1 dataflows
echo "$ALL_DATAFLOWS" | jq '[.[] | select(.generation == 1)]'

# Filter Gen2 dataflows
echo "$ALL_DATAFLOWS" | jq '[.[] | select(.generation == 2)]'

Tip: A modelUrl pointing to dfs.core.windows.net additionally indicates BYOSA (customer-managed storage) — a save-as blocker.

Admin Path (tenant-wide)

Requires Fabric administrator role or service principal with Tenant.Read.All scope. Rate limited to 200 requests/hour.

RESOURCE_PBI="https://analysis.windows.net/powerbi/api"

# List ALL dataflows in the tenant
ADMIN_DATAFLOWS=$(az rest --method get \
  --resource "$RESOURCE_PBI" \
  --url "https://api.powerbi.com/v1.0/myorg/admin/dataflows" \
  --query "value[].{id:objectId, name:name, workspaceId:workspaceId, modelUrl:modelUrl, configuredBy:configuredBy}" \
  -o json)

# Filter Gen1 dataflows — those with a modelUrl indicate CDM/Gen1 storage
# Note: Admin API may not expose the `generation` property; use modelUrl as fallback
echo "$ADMIN_DATAFLOWS" | jq '[.[] | select(.modelUrl != null and .modelUrl != "")]'

Note: The Admin API supports $filter, $top, and $skip for pagination on large tenants.

Step 2: Assess — Check Risk Signals

For each Gen1 dataflow found, evaluate seven risk signals. See risk-assessment-guide.md for detailed API calls.

Step 3: Classify — Readiness Categories

Tip — detect ownership before save-as: The configuredBy field in the dataflow list response contains the owner's email. Compare it against the currently logged-in user (az account show --query user.name -o tsv). If they don't match and your workspace role is below Contributor, flag the dataflow as ❌ Blocked (signal 7) and escalate to the owner.

Step 4: Report — Save-As Readiness Snapshot

Markdown Output (terminal)

## Save-As Readiness Snapshot
| Workspace | Dataflow | Type | Readiness | Risk Signals | Recommendation |
|---|---|---|---|---|---|
| Sales Analytics | SalesETL | Gen1 | ✅ Safe | None | Save as Gen2.1 copy now |
| Sales Analytics | CustomerLoad | Gen1 | ⚠️ Manual | Incremental refresh, Pipeline dep | Save as Gen2.1 copy, then re-enable schedule & update pipeline |
| Finance | FinanceDaily | Gen1 | ❌ Blocked | BYOSA storage | Resolve storage dependency first |

JSON Output (automation)

{
  "snapshotDate": "2025-04-13T10:00:00Z",
  "summary": { "total": 3, "safe": 1, "manual": 1, "blocked": 1 },
  "dataflows": [
    {
      "workspaceName": "Sales Analytics",
      "workspaceId": "...",
      "dataflowName": "SalesETL",
      "dataflowId": "...",
      "type": "Gen1",
      "readiness": "safe",
      "riskSignals": [],
      "recommendation": "Save as Gen2.1 copy now",
      "saveAsPath": "saveAsNativeArtifact"
    }
  ]
}

Save JSON to file: pipe to jq '.' > readiness-snapshot.json

Execute with Guardrails

Goal: Invoke save-as and capture outcomes safely.

Gen1 → Gen2.1 CI/CD: saveAsNativeArtifact API

POST https://api.powerbi.com/v1.0/myorg/groups/{groupId}/dataflows/{gen1DataflowId}/saveAsNativeArtifact

This is a Preview API. It creates a new Gen2.1 CI/CD artifact copy while preserving the original Gen1 dataflow.

WS_ID="<workspaceId>"
GEN1_ID="<gen1DataflowId>"

# Write body to a temp file — az rest wraps inline --body in an envelope
# on some platforms, causing "saveAsRequest is a required parameter" errors.
cat > /tmp/save-as-body.json <<'EOF'
{
  "displayName": "MyDataflow_Gen2CICD",
  "description": "Saved as Gen2.1 copy from Gen1",
  "includeSchedule": true,
  "targetWorkspaceId": "<targetWorkspaceId>"
}
EOF

az rest --method post \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" \
  --headers "Content-Type=application/json" \
  --body @/tmp/save-as-body.json

Gotcha — inline body: Passing JSON inline via --body '{...}' can cause az rest to wrap the payload in an extra envelope, resulting in "saveAsRequest is a required parameter" errors. Always use file-based body (--body @file.json) for this endpoint.

Gotcha — Windows az.cmd: On Windows, omit -o json from saveAsNativeArtifact calls — the flag produces "A value that is not valid (json) was specified for the outputFormat parameter" when routed through az.cmd. Capture output without -o json and parse with ConvertFrom-Json in PowerShell, or pipe to jq in bash.

Gotcha — not idempotent (duplicate artifacts on retry): saveAsNativeArtifact creates a new artifact every time it is called. If a batch is interrupted and re-run, you will end up with multiple copies in the target workspace. To make retries safe: (1) check whether a Gen2 artifact with the intended name already exists before calling, or (2) include a timestamp in displayName and treat each run as a distinct artifact.

Gotcha — owner permissions: You must be the dataflow owner or have Contributor/Admin in the source workspace to call saveAsNativeArtifact. If you are only a Viewer or Workspace Member who does not own the dataflow, the API returns DataflowUnauthorizedError. Ask the dataflow owner or a workspace admin to run the save-as operation for those dataflows.

Request parameters:

Response: 200 OK with SaveAsNativeDataflowResponse:

• artifactMetadata — full metadata of the new Gen2 CI/CD artifact (including objectId, provisionState)
• errors[] — non-fatal warning codes (save-as succeeds even if these occur):
  • FailedToCopySchedule — schedule could not be copied
  • SetDataflowOriginFailed — origin tracking not set
  • ConnectionsUpdateFailed — connection strings could not be updated to Fabric format

Gen2 → Gen2 CI/CD: In-Place Upgrade

NOT YET AVAILABLE — This API is not available in the current public surface. This skill will be updated when the endpoint is published. Do not attempt to call a non-existent endpoint.

Post-Save-As Validation Checklist

Run these checks after save-as before any Gen1 cleanup:

• Confirm artifactMetadata.provisionState reaches Active.
• Review errors[] in SaveAsNativeDataflowResponse and create follow-up tasks for each warning.
• Confirm the new artifact exists in the target workspace and has expected name/description.
• Verify dependent orchestration (pipelines, flows, API callers) is updated to the new artifact ID.
• Only trigger refresh when the user explicitly approves.

Must/Prefer/Avoid

MUST DO

• Always pass --resource to az rest — use the correct audience per the API table above. Wrong audience = silent 401.
• Always include --headers "Content-Type=application/json" on POST calls to the Power BI REST API.
• Use file-based body for saveAsNativeArtifact — pass --body @file.json instead of inline JSON. Inline --body '{...}' can cause az rest to wrap the payload in an extra envelope, producing "saveAsRequest is a required parameter" errors.
• On Windows, omit -o json on saveAsNativeArtifact calls — use ConvertFrom-Json in PowerShell or pipe to jq instead. The -o json flag fails with "A value that is not valid (json)" error when routed through az.cmd.
• Verify you are the dataflow owner or Contributor before save-as — saveAsNativeArtifact returns DataflowUnauthorizedError for non-owners who are only Workspace Members or Viewers.
• Check for existing Gen2 artifacts before retrying — saveAsNativeArtifact is not idempotent; interrupted batch runs create duplicate copies on retry. Either verify the target name is absent before calling, or use a unique timestamped displayName per run.
• Scan before save-as — always run the readiness scan before execution.
• Never refresh without explicit user consent — the Gen2 CI/CD artifact schedule is created in disabled state for safety.
• Check errors[] in saveAsNativeArtifact response — save-as may succeed with non-fatal warnings.
• Verify provisionState is Active after save-as — poll the artifact metadata until terminal state.
• Preserve the original Gen1 dataflow — saveAsNativeArtifact leaves the Gen1 intact. Do not delete it until post-save-as validation passes.

PREFER

• Admin API for tenant-wide scanning — more efficient than workspace-by-workspace for large tenants.
• JSON output for automation — markdown is for human review, JSON for scripting and CI/CD integration.
• Topological save-as order — save upstream dataflows (with linked entities) before downstream consumers.
• Readiness-first execution — do not skip readiness assessment before save-as.
• generation property for Gen1 detection — the Power BI REST API returns generation: 1 or generation: 2 on each dataflow. Use this single-API-call approach over cross-API comparison.
• modelUrl check as secondary signal — dataflows with a modelUrl pointing to dfs.core.windows.net are Gen1 using customer storage (BYOSA blocker).

AVOID

• Saving blocked (❌) dataflows — resolve BYOSA storage and DirectQuery issues first.
• Batch save-as without review — always review the readiness snapshot before saving multiple dataflows.
• Hardcoded workspace/dataflow IDs — resolve dynamically via REST API.
• Ignoring non-fatal errors — FailedToCopySchedule, ConnectionsUpdateFailed, etc. require manual follow-up.
• Calling the Gen2→Gen2 CI/CD in-place save-as API — it does not exist yet. Do not fabricate endpoints.
• Deleting Gen1 artifacts without explicit user consent — never delete Gen1 dataflows unless the user explicitly approves deletion, even after post-save-as validation confirms data integrity.
• Using -o json with saveAsNativeArtifact on Windows — omit the flag; capture raw output and parse it explicitly.
• Re-running batch save-as scripts without idempotency checks — always verify the target artifact doesn't already exist to avoid duplicate copies in the workspace.

Examples

Example 1: Full Phase 1 — Readiness Scan for a Single Workspace

Prompt: "Scan my Sales workspace for Gen1 dataflows and show me a readiness snapshot"

Agent response:

# Step 1 — Resolve workspace ID
WS_ID=$(az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces" \
  --query "value[?displayName=='Sales'].id" -o tsv)

# Step 2 — List all dataflows with generation property
ALL_DFS=$(az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows" -o json)

# Step 3 — Filter Gen1 dataflows by generation property
GEN1_DFS=$(echo "$ALL_DFS" | jq '[.value[] | select(.generation == 1) |
  {id: .objectId, name: .name, generation: .generation, modelUrl: .modelUrl}]')

echo "Gen1 dataflows found: $(echo $GEN1_DFS | jq length)"
echo "$GEN1_DFS" | jq '.'

Example 2: Save-As a Gen1 Dataflow to Gen2.1

Prompt: "Migrate or upgrade the SalesETL Gen1 dataflow in my Sales workspace"

Agent response:

WS_ID="<workspaceId>"
GEN1_ID="<gen1DataflowId>"

# Write body to file (avoids az rest envelope wrapping)
cat > /tmp/save-as-body.json <<'EOF'
{"displayName": "SalesETL_Gen2CICD", "includeSchedule": true}
EOF

# Execute save-as
RESPONSE=$(az rest --method post \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" \
  --headers "Content-Type=application/json" \
  --body @/tmp/save-as-body.json)

# Check result
echo "$RESPONSE" | jq '{
  newId: .artifactMetadata.objectId,
  state: .artifactMetadata.provisionState,
  errors: .errors
}'

Example 3: Tenant-Wide Admin Scan

Prompt: "Show me all Gen1 dataflows across the entire tenant"

Agent response:

# Requires Fabric administrator role
ALL_DFS=$(az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/admin/dataflows" -o json)

# Gen1 dataflows have a modelUrl
echo "$ALL_DFS" | jq '[.value[] | select(.modelUrl != null and .modelUrl != "") |
  {id: .objectId, name: .name, workspace: .workspaceId, 
   owner: .configuredBy, storage: .modelUrl}]'