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Zhongwei Li
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skills/auto-code-review-gate.md
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396
skills/auto-code-review-gate.md
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# Auto Code Review Gate Skill
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## Skill Purpose
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Automatically run comprehensive code reviews before any PR-related commands (`/pr-*`) and ensure all identified issues are resolved before allowing commits to be pushed. This acts as a quality gate to prevent low-quality code from entering the staging/develop branches.
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## Activation
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This skill is automatically triggered when any of these commands are called:
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- `/pr-feature-to-staging`
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- `/pr-deploy-workflow`
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- `/commit-and-pr`
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- `/pr-fix-pr-review`
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- Any other command starting with `/pr-`
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## Workflow
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### Phase 1: Pre-Commit Code Review
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When a `/pr-*` command is detected:
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1. **Intercept the command** - Don't execute the PR command yet
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2. **Display notice to user**:
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```
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🔍 AUTO CODE REVIEW GATE ACTIVATED
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Running comprehensive code review before proceeding with PR...
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This ensures code quality standards are met before merge.
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```
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3. **Execute code review**:
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```bash
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/code-review
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```
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4. **Analyze review results**:
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- Count total issues by severity (Critical, High, Medium, Low)
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- Create issue summary report
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- Determine if auto-fix is possible
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### Phase 2: Issue Resolution
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#### If NO issues found:
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```
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✅ CODE REVIEW PASSED
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No issues detected. Proceeding with original command...
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```
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→ Execute the original `/pr-*` command
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#### If issues found (Critical or High priority):
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```
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❌ CODE REVIEW FAILED - BLOCKING ISSUES FOUND
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Found X critical and Y high-priority issues that must be fixed.
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BLOCKING ISSUES:
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- [List of critical issues with file:line]
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- [List of high-priority issues with file:line]
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🔧 AUTOMATIC FIX PROCESS INITIATED
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Launching pyspark-data-engineer agent to resolve issues...
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```
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**Auto-Fix Workflow**:
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1. **Create task document** (if not already exists):
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- Location: `.claude/tasks/pre_commit_code_review_fixes.md`
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- Format: Same as code review fixes task list
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- Include all critical and high-priority issues
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2. **Launch pyspark-data-engineer agent**:
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```
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Task: Fix all critical and high-priority issues before PR
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Document: .claude/tasks/pre_commit_code_review_fixes.md
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Validation: Run syntax check, linting, and formatting after each fix
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```
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3. **Wait for agent completion** and verify:
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- All critical issues resolved
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- All high-priority issues resolved
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- Syntax validation passes
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- Linting passes
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- No new issues introduced
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4. **Re-run code review** to confirm all issues resolved
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5. **Final decision**:
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- ✅ If all issues fixed: Proceed with original command
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- ❌ If issues remain: Block PR and display unresolved issues
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#### If only Medium/Low priority issues:
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```
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⚠️ CODE REVIEW WARNING - NON-BLOCKING ISSUES FOUND
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Found X medium and Y low-priority issues.
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These won't block the PR but should be addressed soon.
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```
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**User Choice**:
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```
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Do you want to:
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1. Auto-fix these issues before proceeding (recommended)
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2. Proceed with PR and create tech debt ticket
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3. Cancel and fix manually
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Choice [1/2/3]:
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```
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### Phase 3: Post-Fix Validation
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After auto-fix completes:
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1. **Run validation suite**:
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```bash
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python3 -m py_compile <modified_files>
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ruff check python_files/
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ruff format python_files/
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```
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2. **Run second code review**:
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- Ensure no new issues introduced
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- Verify all original issues resolved
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- Check for any regressions
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3. **Generate fix summary**:
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```
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📊 AUTO-FIX SUMMARY
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==================
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Files Modified: 4
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Issues Fixed: 9 (3 critical, 4 high, 2 medium)
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Validation: ✅ All checks passed
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Modified Files:
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- python_files/gold/g_z_mg_occ_person_address.py
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- python_files/gold/g_xa_mg_statsclasscount.py
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- python_files/silver/silver_cms/s_cms_person.py
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- python_files/gold/g_xa_mg_cms_mo.py
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✅ All issues resolved. Proceeding with PR...
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```
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### Phase 4: Execute Original Command
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Only after ALL critical/high issues are resolved:
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1. **Add fixed files to git staging**:
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```bash
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git add <modified_files>
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```
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2. **Create enhanced commit message**:
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```
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[Original commit message]
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🤖 Auto Code Review Fixes Applied:
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- Fixed X critical issues
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- Fixed Y high-priority issues
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- All validation checks passed
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```
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3. **Execute original `/pr-*` command**
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4. **Display completion message**:
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```
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✅ PR CREATED WITH AUTO-FIXES
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All code quality issues have been resolved.
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PR is ready for human review.
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Code Review Report: .claude/tasks/pre_commit_code_review_fixes.md
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```
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## Configuration
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### Severity Thresholds
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```yaml
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# .claude/config/code_review_gate.yaml
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blocking_severities:
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- CRITICAL
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- HIGH
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auto_fix_enabled: true
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auto_fix_medium_issues: true # Prompt user for medium issues
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auto_fix_low_issues: false # Skip low-priority auto-fix
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max_auto_fix_attempts: 2
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validation_required: true
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```
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### Bypass Options
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**Emergency Override** (use with caution):
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```bash
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# Skip code review gate (requires explicit confirmation)
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/pr-feature-to-staging --skip-review-gate --confirm-override
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# This will prompt:
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⚠️ DANGER: Skipping code review gate
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This may introduce bugs or technical debt.
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Type 'I UNDERSTAND THE RISKS' to proceed:
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```
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## Implementation Hooks
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### Hook 1: Command Interceptor
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```python
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# Intercepts all /pr-* commands
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if command.startswith("/pr-"):
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# Trigger auto-code-review-gate skill
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execute_skill("auto-code-review-gate")
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```
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### Hook 2: Issue Detection
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```python
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# Parse code review output
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issues = parse_code_review_output(review_result)
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critical_count = count_by_severity(issues, "CRITICAL")
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high_count = count_by_severity(issues, "HIGH")
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if critical_count > 0 or high_count > 0:
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block_pr = True
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attempt_auto_fix = True
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```
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### Hook 3: Auto-Fix Delegation
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```python
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# Create task document and delegate to pyspark-data-engineer
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task_doc = create_task_document(issues)
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agent_result = launch_agent("pyspark-data-engineer", task_doc)
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# Validate fixes
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validation_passed = run_validation_suite()
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issues_resolved = verify_issues_fixed(issues, agent_result)
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if validation_passed and issues_resolved:
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allow_pr = True
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```
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## Example Execution Flow
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### Scenario: User runs `/pr-feature-to-staging`
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```
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USER: /pr-feature-to-staging "feat: add new statsclasscount table"
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SYSTEM:
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🔍 AUTO CODE REVIEW GATE ACTIVATED
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Running comprehensive code review before proceeding with PR...
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[Code review executes...]
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SYSTEM:
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❌ CODE REVIEW FAILED - 3 CRITICAL ISSUES FOUND
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CRITICAL ISSUES:
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1. python_files/gold/g_z_mg_occ_person_address.py:43
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- Redundant Spark session initialization (memory leak risk)
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2. python_files/gold/g_xa_mg_statsclasscount.py:100
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- Validation methods defined but never called (data quality risk)
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3. python_files/gold/g_z_mg_occ_person_address.py:32
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- Unused constructor parameter (confusing API)
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🔧 AUTOMATIC FIX PROCESS INITIATED
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Launching pyspark-data-engineer agent...
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[Agent fixes all issues...]
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SYSTEM:
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📊 AUTO-FIX SUMMARY
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==================
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Files Modified: 2
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Issues Fixed: 3 (3 critical)
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Validation: ✅ All checks passed
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✅ All critical issues resolved.
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Adding fixed files to commit:
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M python_files/gold/g_z_mg_occ_person_address.py
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M python_files/gold/g_xa_mg_statsclasscount.py
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Proceeding with PR creation...
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[Original /pr-feature-to-staging command executes]
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SYSTEM:
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✅ PR CREATED SUCCESSFULLY
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Branch: feature/statsclasscount → staging
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PR #: 5830
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Status: Ready for review
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All code quality gates passed! 🎉
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```
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## Error Handling
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### If auto-fix fails:
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```
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❌ AUTO-FIX FAILED
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The pyspark-data-engineer agent was unable to resolve all issues.
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Remaining Issues:
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- [List of unresolved issues]
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NEXT STEPS:
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1. Review the task document: .claude/tasks/pre_commit_code_review_fixes.md
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2. Fix issues manually
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3. Re-run /pr-feature-to-staging when ready
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OR
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Use emergency override (not recommended):
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/pr-feature-to-staging --skip-review-gate --confirm-override
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```
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### If validation fails after fix:
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```
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❌ VALIDATION FAILED AFTER AUTO-FIX
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The fixes introduced new issues or broke existing functionality.
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Validation Errors:
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- [List of validation errors]
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Rolling back auto-fixes...
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Original code restored.
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NEXT STEPS:
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1. Review the code review report
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2. Fix issues manually with more care
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3. Test thoroughly before re-running PR command
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```
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## Benefits
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1. **Prevents bugs before merge**: Catches issues at commit time, not in production
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2. **Automated quality gates**: No manual intervention needed for common issues
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3. **Consistent code quality**: All PRs meet minimum quality standards
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4. **Faster review cycles**: Human reviewers see clean code
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5. **Learning tool**: Developers see fixes and learn patterns
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6. **Tech debt prevention**: Issues fixed immediately, not deferred
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## Metrics Tracked
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The skill automatically logs:
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- Number of PRs with code review issues
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- Issues caught per severity level
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- Auto-fix success rate
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- Time saved by automated fixes
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- Common issue patterns
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Stored in: `.claude/metrics/code_review_gate_stats.json`
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## Integration with Existing Workflows
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This skill works seamlessly with:
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- `/pr-feature-to-staging` - Adds quality gate before PR creation
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- `/pr-deploy-workflow` - Ensures clean code through entire deployment pipeline
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- `/commit-and-pr` - Quick commits still get quality checks
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- `/pr-fix-pr-review` - Prevents re-introducing issues when fixing review feedback
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## Testing the Skill
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To test the auto code review gate:
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```bash
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# 1. Make some intentional code quality issues
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echo "import os\nimport os" >> test_file.py # Duplicate import
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# 2. Try to create PR
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/pr-feature-to-staging "test auto review gate"
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# 3. Verify gate catches issues and auto-fixes them
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# 4. Confirm PR only proceeds after fixes applied
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```
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## Maintenance
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Update the skill when:
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- New code quality rules are added
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- Project standards change
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- New file types need review
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- Additional validation checks needed
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## Future Enhancements
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Potential improvements:
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1. **AI-powered issue prioritization**: Use ML to determine which issues are most critical
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2. **Team notification**: Slack/Teams alerts when auto-fixes are applied
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3. **Fix explanation**: Include detailed explanations of each fix for learning
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4. **Custom rule sets**: Project-specific or team-specific quality gates
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5. **Performance metrics**: Track build times and code quality trends
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---
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**Status**: Active
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**Version**: 1.0
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**Last Updated**: 2025-11-04
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**Owner**: DevOps/Quality Team
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Reference in New Issue
Block a user