architecture-design

# Architecture Design - ML Project Template This skill defines the standard code architecture for machine learning projects based on the template structure. When modifying or extending code, follow these patterns to maintain consistency. ## Overview The project follows a modular, extensible architecture with clear separation of concerns. Each module (data, model, trainer, analysis) is independently organized using factory and registry patterns for maximum flexibility. ## When to Use Use this skill when: - Creating a new Dataset class that needs `@register_dataset` - Creating a new Model class that needs `@register_model` - Creating a new module directory with `__init__.py` factory wiring - Initializing a new ML project structure from scratch - Adding new component types such as Augmentation, CollateFunction, or Metrics ## When Not to Use Do not use this skill when: - Modifying existing functions or methods - Fixing bugs in existing code - Adding helper functions or utilities - Refactoring without adding new registrable components - Making simple code changes to a single file - Modifying configuration files - Reading or understanding existing code Key indicator: if the task does not require a `@register_*` decorator or a Factory pattern, skip this skill. ## Core Design Patterns ### Factory Pattern Each module uses a factory to create instances dynamically: ```python # Example from data_module/dataset/__init__.py DATASET_FACTORY: Dict = {} def DatasetFactory(data_n...