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

Data Engineering Consumption — CLI Skill

Table of Contents

storage.azure.com

az login

az rest

--resource https://api.fabric.microsoft.com

az rest

curl

curl

az rest

sqlcmd

az rest

az rest

--resource

storage.azure.com

Must/Prefer/Avoid

MUST DO

• Check for existing idle sessions before creating new ones
• Use dynamic workspace/lakehouse discovery
• Follow API patterns from COMMON-CLI.md

PREFER

• sqldw-consumption-cli for simple lakehouse queries — row counts, SELECT, schema exploration, filtering, and aggregation on lakehouse Delta tables should use the SQL Endpoint via

  sqlcmd

  , not Spark. Only use this skill when the user explicitly requests PySpark, DataFrames, or Spark-specific features.
• SQL Endpoint for Delta tables
• Livy for unstructured/JSON data or complex Python analytics
• Session reuse over creation

AVOID

• Hardcoded workspace IDs
• Creating unnecessary sessions
• Large result sets without LIMIT

Quick Start

Environment Setup

Apply environment detection from COMMON-CORE.md Environment Detection Pattern to set:

• $FABRIC_API_BASE

  and

  $FABRIC_RESOURCE_SCOPE

• $FABRIC_API_URL

  and

  $LIVY_API_PATH

  for Livy operations

Authentication: Use token acquisition from COMMON-CLI.md Environment Detection and API Configuration

Workspace & Item Discovery

Preferred: Use COMMON-CLI.md item discovery patterns (Finding things in Fabric) to find workspaces and items by name.

Fallback (when workspace is already known):

# List workspaces
az rest --method get --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces" --query "value[].{name:displayName, id:id}" --output table
read -p "Workspace ID: " workspaceId

# List lakehouses in workspace
az rest --method get --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/items?type=Lakehouse" --query "value[].{name:displayName, id:id}" --output table  
read -p "Lakehouse ID: " lakehouseId

Session Management

# Check for existing idle session (avoid resource waste)
sessionId=$(az rest --method get --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/lakehouses/$lakehouseId/$LIVY_API_PATH/sessions" --query "sessions[?state=='idle'][0].id" --output tsv)

# Create if none available - FORCE STARTER POOL USAGE
if [[ -z "$sessionId" ]]; then
    cat > /tmp/body.json << 'EOF'
{
    "name":"analysis",
    "driverMemory":"56g",
    "driverCores":8,
    "executorMemory":"56g",
    "executorCores":8,
    "conf": {
        "spark.dynamicAllocation.enabled": "true",
        "spark.fabric.pool.name": "Starter Pool"
    }
}
EOF
    sessionId=$(az rest --method post --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/lakehouses/$lakehouseId/$LIVY_API_PATH/sessions" --body @/tmp/body.json --query "id" --output tsv)
    
    echo "⏳ Waiting for starter pool session to be ready..." 
    # With starter pools, this should be 3-5 seconds
    timeout=30  # Reduced from 90s since starter pools are fast
    while [ $timeout -gt 0 ]; do
        state=$(az rest --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/lakehouses/$lakehouseId/$LIVY_API_PATH/sessions/$sessionId" --query "state" --output tsv)
        if [[ "$state" == "idle" ]]; then
            echo "✅ Session ready in starter pool!"
            break
        fi
        echo "   Session state: $state (${timeout}s remaining)"
        sleep 3
        timeout=$((timeout - 3))
    done
fi

Data Exploration (Fabric-Specific Patterns)

# Execute statement (LLM knows Python/Spark syntax)
cat > /tmp/body.json << 'EOF'
{
  "code": "spark.sql(\"SHOW TABLES\").show(); df = spark.table(\"your_table\"); df.describe().show()",
  "kind": "pyspark"
}
EOF
az rest --method post --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/lakehouses/$lakehouseId/$LIVY_API_PATH/sessions/$sessionId/statements" --body @/tmp/body.json

Key Fabric Patterns

spark.sql("SHOW TABLES")

spark.sql("SELECT * FROM other_workspace.table")

df.history()

df.readVersion(1)

df.printSchema()

Session Cleanup

# Clean up idle sessions (optional)
az rest --method get --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/lakehouses/$lakehouseId/$LIVY_API_PATH/sessions" --query "sessions[?state=='idle'].id" --output tsv | xargs -I {} az rest --method delete --resource "$FABRIC_RESOURCE_SCOPE" --url "$FABRIC_API_URL/workspaces/$workspaceId/lakehouses/$lakehouseId/$LIVY_API_PATH/sessions/{}"

Focus: This skill provides Fabric-specific REST API patterns. LLM already knows Python/Spark syntax — we focus on Fabric integration, session management, and API endpoints.