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architecture-design

Register ML components via factory and registry patterns with decorators.

Updated Mar 27, 2026
Search Tags:#architecture#auto-import#registry-pattern#factory-pattern#component-registration#ml-component
One-click install
npx skills add https://github.com/EmaRimoldi/Claude-scholar-extended --skill architecture-design-emarimoldi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architecture-design
Source: https://github.com/EmaRimoldi/Claude-scholar-extended/tree/main/skills/architecture-design
Command: npx skills add https://github.com/EmaRimoldi/Claude-scholar-extended --skill architecture-design-emarimoldi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill standardizes ML project architecture by enforcing factory and registry patterns to enable dynamic, extensible component registration and discovery.

Core Features & Use Cases

  • Factory & Registry patterns: centralize creation and registration of datasets, models, and modules to enable easy extension.
  • Auto-discovery & modularity: modules auto-import to populate registries, reducing manual wiring.
  • Use Case: when adding a new dataset or model, define a registrar-decorated class and it will be automatically available through the factory.

Quick Start

Create a new registrable component by decorating it with the appropriate @register_* decorator and rely on auto-import to register it with the central factory.

Frequently Asked Questions about architecture-design

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

FAQPage Schema
How do I use registry patterns to dynamically register ML components?▼

You can dynamically register ML components by decorating new Dataset or Model classes, which enables automatic discovery and instantiation through a central factory without manual wiring.

What is the best way to add a new model or dataset to an ML project without manual wiring?▼

The best way to add new ML models or datasets is to define a registrar-decorated class, which uses auto-import to automatically populate the central registry and make the component instantly available.

How does auto-import work for populating factory patterns in machine learning projects?▼

Auto-import populates factory registries by automatically discovering and loading modules, reducing manual configuration when extending machine learning project architectures with new components.

Does the architecture-design skill require external dependencies for component registration?▼

No, the architecture-design skill requires zero external dependencies to standardize ML project architecture, relying solely on native factory and registry patterns for component registration.

Can I include optional references or scripts when defining ML component classes?▼

Yes, you can include optional references, scripts, and assets when defining registrable ML component classes, as the registration process fully supports satisfying frontmatter requirements.

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