skills-for-fabric/.windsurfrules at main · microsoft/skills-for-fabric · GitHub

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# Microsoft Fabric Development Rules for Windsurf

# Update Check: At session start, check for skills-for-fabric updates by reading the
# remote package.json version from https://github.com/gim-home/skills-for-fabric
# (via "git fetch origin main --quiet && git show origin/main:package.json"
# or GitHub API with authentication) and comparing with the local package.json
# version. Show changelog if update available.

project_context: |
  This project uses Microsoft Fabric for data engineering, warehousing, and analytics.
  Primary API reference: https://learn.microsoft.com/en-us/rest/api/fabric/articles/
  For authentication and deployment patterns, see DEVELOPMENT-GUIDE.md at repository root.

rules:
  # Lakehouse & Spark
  - name: delta_lake_format
    description: Always use Delta Lake format for Lakehouse tables
    pattern: "spark.write.format(\"delta\")"

  - name: mssparkutils_usage
    description: Use mssparkutils for Fabric-specific operations
    applies_to: ["*.py", "*.ipynb"]

  - name: spark_optimization
    description: Run OPTIMIZE and VACUUM for Delta table maintenance

  # Warehouse
  - name: tsql_surface_area
    description: Check T-SQL surface area - not all SQL Server features supported
    reference: https://learn.microsoft.com/en-us/fabric/data-warehouse/tsql-surface-area

  - name: limit_queries
    description: Always use TOP/LIMIT when exploring data
    pattern: "SELECT TOP"

  # KQL
  - name: kql_time_filter
    description: Always include time filters in KQL queries
    pattern: "where Timestamp > ago("

  # Security
  - name: no_hardcoded_secrets
    description: Never hardcode credentials - use Key Vault or environment variables
    forbidden_patterns:
      - "password="
      - "secret="
      - "connectionstring="

  # Architecture
  - name: medallion_architecture
    description: Prefer Bronze/Silver/Gold data organization

best_practices:
  - Use parameterized notebooks and pipelines
  - Implement incremental processing over full refreshes
  - Document DAX measures with comments
  - Use REST APIs for programmatic Fabric management

documentation_links:
  lakehouse: https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview
  warehouse: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-warehousing
  notebooks: https://learn.microsoft.com/en-us/fabric/data-engineering/how-to-use-notebook
  pipelines: https://learn.microsoft.com/en-us/fabric/data-factory/data-factory-overview
  kql: https://learn.microsoft.com/en-us/fabric/real-time-intelligence/create-database
  semantic_models: https://learn.microsoft.com/en-us/power-bi/connect-data/service-datasets-understand
  data_agents: https://learn.microsoft.com/en-us/fabric/data-science/concept-data-agent
  data_agent_evaluation: https://learn.microsoft.com/en-us/fabric/data-science/fabric-data-agent-sdk