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architecture-design

Standardize ML project architecture with factory and registry patterns.

5|2|Updated Jul 2, 2026
Search Tags:#architecture#modular-design#auto-import#ml-project#registry-pattern#factory-pattern
One-click install
npx skills add https://github.com/Tx1207/hello-scholar --skill architecture-design-tx1207
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: architecture-design
Source: https://github.com/Tx1207/hello-scholar/tree/main/skills/development/architecture-design
Command: npx skills add https://github.com/Tx1207/hello-scholar --skill architecture-design-tx1207

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill includes references (resource) components.

What problem does it solve?

The skill provides a blueprint for building scalable ML projects by enforcing factory and registry patterns to manage registrable components and prevent ad-hoc module growth.

Core Features & Use Cases

  • Factory Pattern enables dynamic creation and central registration of components (datasets, models, augmentations) across modules.
  • Registry Pattern allows declarative component registration via decorators for easy discovery.
  • Auto-Import ensures all modules are loaded so registries are populated at runtime.
  • Use cases include creating new registries, adding registrable components, and maintaining consistent project structure.

Quick Start

Create a new registrable ML component using the appropriate decorator (e.g., @register_dataset or @register_model) and rely on auto-import to register it automatically.

Frequently Asked Questions about architecture-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure an ML project to prevent ad-hoc module growth?โ–ผ

Applying factory and registry patterns standardizes ML project architecture by enforcing consistent module boundaries and managing registrable components to prevent ad-hoc module growth.

How do I dynamically register new datasets and models in an ML project?โ–ผ

You register new datasets and models declaratively via decorator-based registry patterns, relying on auto-import to ensure all modules load and populate the component registries at runtime.

What is the registry pattern for ML component management?โ–ผ

The registry pattern for ML component management allows declarative component registration via decorators, enabling dynamic creation and central registration of datasets, models, and augmentations across modules.

How does auto-import ensure registered ML components are discovered?โ–ผ

Auto-import ensures registered ML components are discovered by automatically loading all modules at runtime, which populates the registries so components can be dynamically created and centrally registered.

Does this ML architecture approach work for registering augmentations?โ–ผ

Yes, this ML architecture approach works for registering augmentations. The factory pattern enables dynamic creation and central registration of augmentations alongside datasets and models across modules.

When should I not use factory and registry patterns in an ML project?โ–ผ

You should avoid factory and registry patterns in an ML project when your component count is small and static, as the auto-import and decorator overhead provides little scaling benefit for simple use cases.

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