gh-linus-mcmanamey-unify-2-1-plugin/commands/pr-review.md

model: claude-haiku-4-5-20251001 allowed-tools: Bash(git branch:), Bash(git status:), Bash(git log:), Bash(git diff:), mcp__*, mcp__ado__repo_get_repo_by_name_or_id, mcp__ado__repo_list_pull_requests_by_repo_or_project, mcp__ado__repo_get_pull_request_by_id, mcp__ado__repo_list_pull_request_threads, mcp__ado__repo_list_pull_request_thread_comments, mcp__ado__repo_create_pull_request_thread, mcp__ado__repo_reply_to_comment, mcp__ado__repo_update_pull_request, mcp__ado__repo_search_commits, mcp__ado__pipelines_get_builds, Read, Task argument-hint: [PR_ID] (optional - if not provided, will list all open PRs) # PR Review and Approval

Task

Review open pull requests in the current repository and approve/complete them if they meet quality standards.

Instructions

1. Get Repository Information

• Use mcp__ado__repo_get_repo_by_name_or_id with:
  • Project: Program Unify
  • Repository: unify_2_1_dm_synapse_env_d10
• Extract repository ID: d3fa6f02-bfdf-428d-825c-7e7bd4e7f338

2. List Open Pull Requests

• Use mcp__ado__repo_list_pull_requests_by_repo_or_project with:
  • Repository ID: d3fa6f02-bfdf-428d-825c-7e7bd4e7f338
  • Status: Active
• If $ARGUMENTS provided, filter to that specific PR ID
• Display all open PRs with key details (ID, title, source/target branches, author)

3. Review Each Pull Request

For each PR (or the specified PR):

3.1 Get PR Details

• Use mcp__ado__repo_get_pull_request_by_id to get full PR details
• Check merge status - if conflicts exist, stop and report

3.2 Get PR Changes

• Use mcp__ado__repo_search_commits to get commits in the PR
• Identify files changed and scope of changes

3.3 Review Code Quality

Read changed files and evaluate:

1. Code Quality & Maintainability

   • Proper use of type hints and descriptive variable names
   • Maximum line length (240 chars) compliance
   • No blank lines inside functions
   • Proper import organization
   • Use of @synapse_error_print_handler decorator
   • Proper error handling with meaningful messages

2. PySpark Best Practices

   • DataFrame operations over raw SQL
   • Proper use of TableUtilities methods
   • Correct logging with NotebookLogger
   • Proper session management

3. ETL Pattern Compliance

   • Follows ETL class pattern for Silver/Gold layers
   • Proper extract/transform/load method structure
   • Correct database and table naming conventions

4. Standards Compliance

   • Follows project coding standards from .claude/rules/python_rules.md
   • No missing docstrings (unless explicitly instructed to omit)
   • Proper use of configuration from configuration.yaml

3.4 Review DevOps Considerations

1. CI/CD Integration

   • Changes compatible with existing pipeline
   • No breaking changes to deployment process

2. Configuration & Infrastructure

   • Proper environment detection pattern
   • Azure integration handled correctly
   • No hardcoded paths or credentials

3. Testing & Quality Gates

   • Syntax validation would pass
   • Linting compliance (ruff check)
   • Test coverage for new functionality

3.5 Deep PySpark Analysis (Conditional)

Only execute if PR modifies PySpark ETL code

Check if PR changes affect:

• python_files/pipeline_operations/bronze_layer_deployment.py
• python_files/pipeline_operations/silver_dag_deployment.py
• python_files/pipeline_operations/gold_dag_deployment.py
• Any files in python_files/silver/
• Any files in python_files/gold/
• python_files/utilities/session_optimiser.py

If PySpark files are modified, use Task tool to launch pyspark-engineer agent:

Task tool parameters:
- subagent_type: "pyspark-engineer"
- description: "Deep PySpark analysis for PR #[PR_ID]"
- prompt: "
  Perform expert-level PySpark analysis for PR #[PR_ID]:

  PR Details:
  - Title: [PR_TITLE]
  - Changed Files: [LIST_OF_CHANGED_FILES]
  - Source Branch: [SOURCE_BRANCH]
  - Target Branch: [TARGET_BRANCH]

  Review Requirements:
  1. Read all changed PySpark files
  2. Analyze transformation logic for:
     - Partitioning strategies and data skew
     - Shuffle optimisation opportunities
     - Broadcast join usage and optimisation
     - Memory management and caching strategies
     - DataFrame operation efficiency
  3. Validate Medallion Architecture compliance:
     - Bronze layer: Raw data preservation patterns
     - Silver layer: Cleansing and standardization
     - Gold layer: Business model optimisation
  4. Check performance considerations:
     - Identify potential bottlenecks
     - Suggest optimisation opportunities
     - Validate cost-efficiency patterns
  5. Verify test coverage:
     - Check for pytest test files
     - Validate test completeness
     - Suggest missing test scenarios
  6. Review production readiness:
     - Error handling for data pipeline failures
     - Idempotent operation design
     - Monitoring and logging completeness

  Provide detailed findings in this format:

  ## PySpark Analysis Results

  ### Critical Issues (blocking)
  - [List any critical performance or correctness issues]

  ### Performance Optimisations
  - [Specific optimisation recommendations]

  ### Architecture Compliance
  - [Medallion architecture adherence assessment]

  ### Test Coverage
  - [Test completeness and gaps]

  ### Recommendations
  - [Specific actionable improvements]

  Return your analysis for integration into the PR review.
  "

Integration of PySpark Analysis:

• If pyspark-engineer identifies critical issues → Add to review comments
• If optimisations suggested → Add as optional improvement comments
• If architecture violations found → Add as required changes
• Include all findings in final review summary

4. Provide Review Comments

• Use mcp__ado__repo_list_pull_request_threads to check existing review comments
• If issues found, use mcp__ado__repo_create_pull_request_thread to add:
  • Specific file-level comments with line numbers
  • Clear description of issues
  • Suggested improvements
  • Mark as Active status if changes required

5. Approve and Complete PR (if satisfied)

Only proceed if ALL criteria met:

• No merge conflicts
• Code quality standards met
• PySpark best practices followed
• ETL patterns correct
• No DevOps concerns
• Proper error handling and logging
• Standards compliant
• PySpark analysis (if performed) shows no critical issues
• Performance optimisations either implemented or deferred with justification
• Medallion architecture compliance validated

If approved:

1. Use mcp__ado__repo_update_pull_request with:

   • Set autoComplete: true
   • Set mergeStrategy: "NoFastForward" (or "Squash" if many small commits)
   • Set deleteSourceBranch: false (preserve branch history)
   • Set transitionWorkItems: true
   • Add approval comment explaining what was reviewed

2. Confirm completion with summary:

   • PR ID and title
   • Number of commits reviewed
   • Key changes identified
   • Approval rationale

6. Report Results

Provide comprehensive summary:

• Total open PRs reviewed
• PRs approved and completed (with IDs)
• PRs requiring changes (with summary of issues)
• PRs blocked by merge conflicts
• PySpark analysis findings (if performed)
• Performance optimisation recommendations

Important Notes

• No deferrals: All identified issues must be addressed before approval
• Immediate action: If improvements needed, request them now - no "future work" comments
• Thorough review: Check both code quality AND DevOps considerations
• Professional objectivity: Prioritize technical accuracy over validation
• Merge conflicts: Do NOT approve PRs with merge conflicts - report them for manual resolution