# Tag-Based Filtering Feature - Specification Complete ✅

## Summary

The comprehensive specification for tag-based filtering has been successfully created and is ready for implementation. This feature will enable users to filter AWS resources by tags when generating diagrams, addressing both service-specific and account-wide filtering use cases.

## Specification Location

All specification files are located in `.kiro/specs/tag-based-filtering/`:

- **requirements.md** - 10 comprehensive requirements with 50+ acceptance criteria
- **design.md** - Complete architecture, data models, and 12 correctness properties
- **tasks.md** - 15 main tasks with 25 total tasks (including all tests)

## Your Use Cases Covered

### 1. Service-Specific Filtering (EC2 Example)
Filter only EC2 resources while including all other services:

```bash
# Filter EC2 instances by environment tag
~/.local/bin/reverse_diagrams --service-tag ec2 Environment=Production -p labvel-master -r us-east-1

# Filter EC2 with Organizations and Identity Center unfiltered
~/.local/bin/reverse_diagrams -o -i --service-tag ec2 Environment=Production -p labvel-master -r us-east-1
```

### 2. Account-Wide Filtering
Apply tag filters across all AWS services:

```bash
# Filter all services by environment tag
~/.local/bin/reverse_diagrams -o -i --tag Environment=Production -p labvel-master -r us-east-1

# Multiple tags with AND logic (default)
~/.local/bin/reverse_diagrams -o -i --tag Environment=Production --tag Team=DevOps -p labvel-master -r us-east-1

# Multiple tags with OR logic
~/.local/bin/reverse_diagrams -o -i --tag Environment=Production --tag Environment=Staging --tag-logic OR -p labvel-master -r us-east-1
```

## Key Features Designed

### Filtering Options
- **Key-value filters**: `--tag Environment=Production`
- **Key-only filters**: `--tag-key CostCenter`
- **Exclude filters**: `--exclude-tag Status=Deprecated`
- **Service-specific**: `--service-tag ec2 Environment=Production`
- **Logical operations**: `--tag-logic AND` or `--tag-logic OR`

### Advanced Capabilities
- **Preset Management**: Save and reuse common filter configurations
- **Performance Optimization**: Early filtering during data collection
- **Relationship Preservation**: Maintains resource relationships after filtering
- **Error Handling**: Clear feedback for invalid filters or empty results
- **Plugin Integration**: Standardized interface for all current and future plugins

## Implementation Tasks

The implementation is broken down into 15 main tasks:

1. **Core Data Models** - TagFilter, TagFilterSet, FilterResult classes
2. **CLI Parser** - Parse tag filter arguments from command line
3. **Filter Engine** - Central coordinator for all filtering operations
4. **Plugin Interface** - Extend base plugin class with filtering support
5. **EC2 Plugin Integration** - Add tag filtering to EC2 plugin
6. **Organizations Plugin Integration** - Add tag filtering to Organizations plugin
7. **Identity Center Plugin Integration** - Add tag filtering to Identity Center plugin
8. **Preset Manager** - Save and load filter configurations
9. **CLI Integration** - Update main CLI with tag filtering options
10. **Performance Optimizations** - Caching and early filtering
11. **Error Handling** - Comprehensive validation and user feedback
12. **Documentation** - Help text, examples, and troubleshooting guide
13. **Checkpoint** - Ensure all tests pass
14. **Integration Testing** - End-to-end validation
15. **Final Checkpoint** - Final validation

Each task includes:
- Clear implementation requirements
- Property-based tests (100+ iterations)
- Unit tests for specific scenarios
- Requirements traceability

## Testing Strategy

### Property-Based Tests (12 properties)
- Tag filter matching consistency
- Logical operations (AND/OR)
- Include/exclude precedence
- Service isolation
- Relationship preservation
- Filter validation
- CLI parsing
- Performance optimization
- Preset operations
- Plugin interface consistency
- Empty result handling
- Graceful service degradation

### Integration Tests
- Multi-service filtering scenarios
- Large resource set performance
- Preset management functionality
- Backward compatibility verification

## Next Steps

### To Begin Implementation:

1. **Open the tasks file**:
   ```bash
   code .kiro/specs/tag-based-filtering/tasks.md
   ```

2. **Start with Task 1**: Create core tag filtering data models
   - Location: `src/filters/tag_filter_models.py`
   - Implement `TagFilter`, `TagFilterSet`, and `FilterResult` classes

3. **Follow the task order**: Each task builds on the previous ones

4. **Run tests after each task**: Ensure incremental validation

### Current Package Status

✅ **Package Fixed and Reinstalled**
- Import issues resolved (relative imports restored)
- Package reinstalled via pipx
- Command available at: `~/.local/bin/reverse_diagrams`
- Version: 1.3.5

### Plugin Architecture Status

✅ **Plugin Migration Complete**
- Organizations plugin: Fully functional
- Identity Center plugin: Fully functional
- EC2 plugin: Ready for tag filtering integration
- Plugin discovery: Working correctly
- Backward compatibility: Maintained

## Architecture Benefits

The tag-based filtering feature integrates seamlessly with the existing plugin architecture:

1. **Extensibility**: Easy to add filtering to new plugins
2. **Consistency**: Standardized filtering behavior across all services
3. **Performance**: Optimized with early filtering and caching
4. **Usability**: Intuitive CLI options with presets
5. **Maintainability**: Clear separation of concerns

## Documentation

Comprehensive documentation will be created covering:
- CLI usage examples for common scenarios
- Filter syntax and logical operations
- Preset management and sharing
- Troubleshooting common issues
- Performance optimization tips

## Conclusion

The tag-based filtering specification is complete and ready for implementation. The design addresses your specific use cases while providing a flexible, performant, and extensible solution that integrates seamlessly with the existing plugin architecture.

You can now begin implementing the tasks in order, starting with the core data models and building incrementally toward the full feature. Each task has clear requirements, comprehensive testing, and traceability to the original requirements.

**Happy coding! 🚀**