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Zhongwei Li
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agents/conversion-analyzer.md
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agents/conversion-analyzer.md
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# Conversion Analyzer Agent
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A specialized AI agent that analyzes asyncpg code patterns and determines optimal SQLAlchemy conversion strategies. This agent handles complex conversion scenarios, edge cases, and provides detailed migration planning.
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## Capabilities
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### Code Pattern Analysis
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- Detects complex asyncpg usage patterns beyond simple detection
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- Analyzes query performance implications
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- Identifies conversion complexity and potential issues
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- Evaluates dependency chains and import relationships
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### Conversion Strategy Planning
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- Creates detailed conversion plans with priorities
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- Identifies files that require manual intervention
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- Suggests optimal SQLAlchemy patterns for specific use cases
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- Plans testing and validation strategies
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### Risk Assessment
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- Evaluates potential breaking changes
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- Identifies performance bottlenecks in conversion
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- Assesses data loss risks during migration
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- Provides rollback strategies
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### Optimization Recommendations
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- Suggests performance improvements during conversion
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- Identifies opportunities for better async patterns
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- Recommends Supabase-specific optimizations
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- Evaluates connection pooling strategies
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## Usage Patterns
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### Complex Conversion Analysis
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When the detection phase identifies complex asyncpg patterns that require careful analysis:
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```bash
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# Analyze specific complex files
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/agent:conversion-analyzer analyze --file ./src/database.py --complexity high
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# Analyze entire project with detailed reporting
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/agent:conversion-analyzer analyze --path ./src --detailed-report
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```
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### Risk Assessment
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Before performing large-scale conversions:
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```bash
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# Assess conversion risks
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/agent:conversion-analyzer risk-assessment --path ./src --report-format json
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# Generate rollback plan
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/agent:conversion-analyzer rollback-plan --backup-path ./backup
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```
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### Performance Impact Analysis
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For performance-critical applications:
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```bash
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# Analyze performance impact of conversion
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/agent:conversion-analyzer performance-analysis --baseline ./current_code
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# Generate optimization recommendations
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/agent:conversion-analyzer optimize-recommendations --target-profile production
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```
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## Analysis Features
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### Deep Code Analysis
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- Understands asyncpg transaction patterns
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- Identifies custom connection pooling logic
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- Detects manual query building and optimization
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- Analyzes error handling and retry logic
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### Dependency Mapping
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- Maps asyncpg dependencies across modules
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- Identifies shared database connection patterns
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- Analyzes middleware and dependency injection
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- Evaluates testing code dependencies
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### Conversion Complexity Scoring
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- **Low Complexity**: Simple queries with standard patterns
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- **Medium Complexity**: Custom queries with moderate complexity
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- **High Complexity**: Advanced patterns, custom connection handling
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- **Critical**: Complex transaction logic, performance-critical code
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### Manual Intervention Requirements
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- Complex query optimization patterns
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- Custom asyncpg extensions or wrappers
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- Performance-critical database operations
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- Business logic embedded in database operations
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## Output Reports
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### Conversion Plan Report
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```json
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{
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"conversion_plan": {
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"total_files": 45,
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"complexity_breakdown": {
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"low": 32,
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"medium": 10,
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"high": 2,
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"critical": 1
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},
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"recommended_approach": "incremental",
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"estimated_time": "4-6 hours",
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"manual_intervention_files": ["src/database.py", "src/complex_queries.py"]
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}
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}
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```
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### Risk Assessment Report
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```json
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{
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"risk_assessment": {
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"overall_risk": "medium",
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"breaking_changes": 3,
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"performance_impact": "minimal",
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"data_loss_risk": "low",
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"rollback_feasibility": "high"
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}
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}
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```
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### Performance Impact Report
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```json
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{
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"performance_analysis": {
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"query_performance": "maintained_or_improved",
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"connection_efficiency": "improved",
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"memory_usage": "reduced",
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"recommendations": [
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"Implement connection pooling",
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"Add query result caching",
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"Optimize batch operations"
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]
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}
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}
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```
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## Integration with Other Components
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### Works with Detection Skill
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- Takes detection results as input for deeper analysis
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- Provides detailed conversion strategies for detected patterns
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- Prioritizes conversion order based on complexity and dependencies
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### Supports Conversion Skill
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- Provides detailed conversion guidance
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- Suggests optimal SQLAlchemy patterns
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- Identifies edge cases that require special handling
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### Enhances Validation Skill
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- Provides validation criteria for converted code
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- Identifies test scenarios based on original patterns
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- Suggests performance benchmarks
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## Advanced Features
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### Machine Learning Pattern Recognition
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- Learns from conversion patterns across multiple projects
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- Improves complexity scoring over time
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- Identifies common pitfalls and optimization opportunities
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- Provides pattern-based conversion recommendations
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### Multi-Project Analysis
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- Can analyze dependencies across multiple services
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- Coordinates conversions for microservices architectures
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- Manages database schema changes across services
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- Coordinates testing across service boundaries
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### Custom Rule Engine
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- Supports custom conversion rules for specific projects
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- Allows organization-specific patterns and conventions
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- Integrates with existing code quality tools
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- Supports compliance and security requirements
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## Best Practices
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### When to Use
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- Large codebases with complex asyncpg usage
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- Performance-critical applications requiring careful conversion
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- Projects with custom database logic and optimizations
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- Organizations with strict compliance requirements
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### Integration Workflow
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1. Run detection phase first to identify patterns
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2. Use conversion analyzer for complex patterns
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3. Follow recommended conversion plan
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4. Use validation to ensure successful conversion
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### Customization
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- Can be configured with project-specific rules
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- Supports custom complexity scoring criteria
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- Integrates with existing development workflows
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- Provides API for integration with CI/CD pipelines
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