skills.rest
HaipingXu
HaipingXuCOMMUNITY
Explore all skills by this author →
GitHub

architecture-design

Define modular ML project architectures using factory and registry patterns.

21|4|Updated Mar 14, 2026
Search Tags:#project structure#python#mlops#code architecture#factory pattern#registry pattern
One-click install
npx skills add https://github.com/HaipingXu/social-science-claude-scholar --skill architecture-design-haipingxu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architecture-design
Source: https://github.com/HaipingXu/social-science-claude-scholar/tree/main/skills/architecture-design
Command: npx skills add https://github.com/HaipingXu/social-science-claude-scholar --skill architecture-design-haipingxu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized, modular, and extensible code architecture for machine learning projects, ensuring consistency and maintainability.

Core Features & Use Cases

  • Factory and Registry Patterns: Dynamically create and manage components like datasets and models.
  • Modular Directory Structure: Organizes code into logical modules (data, model, trainer, analysis) for clarity.
  • Config-Driven Design: Emphasizes using configuration files (YAML) for all hyperparameters and settings.
  • Use Case: When starting a new ML project or adding a new component (like a custom dataset or model), this Skill guides you on how to structure the code, register it, and configure it properly.

Quick Start

Use the architecture-design skill to understand how to create a new dataset class using the registry pattern.

Frequently Asked Questions about architecture-design

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

FAQPage Schema
How do I structure a Python ML project for MLOps and maintainability?▼

To structure a Python ML project for MLOps, organize code into modular directories for data, models, trainers, and analysis, while using config-driven YAML files for all hyperparameters and settings.

What is the registry pattern in machine learning code architecture?▼

The registry pattern in machine learning code architecture is a mechanism for dynamic component creation and management, enabling auto-import for seamless discovery and registration of custom datasets and models.

How do I add a new custom dataset or model using a factory pattern?▼

To add a new custom dataset or model using a factory pattern, define the new component class, register it within the system's registry, and configure its hyperparameters using a YAML configuration file.

Does config-driven ML design require YAML files for training pipelines?▼

Config-driven ML design emphasizes using YAML files for all hyperparameters and settings to ensure consistency, maintainability, and standardized training pipelines across different machine learning components.

When should I use a modular directory structure for my ML project?▼

You should use a modular directory structure for your ML project when starting a new machine learning initiative or adding new components, ensuring logical organization into data, model, trainer, and analysis modules.

Related Skills

Explore More Software Engineering Skills→
iamcapoteiamcapote

setup-stellar-contracts

Automate Stellar Soroban contract setup with Rust toolchains and OpenZeppelin crates.

Community
Advanced
k1lgork1lgor
2

ml-engineer

Automate end-to-end ML workflows from raw data to deployed models.

Community
Advanced
ScientiaCapitalScientiaCapital
1

adaptive-workflows

Track experiment results and generate reusable workflow templates.

Community
Advanced
Tx1207Tx1207
5

architecture-design

Standardize ML project architecture with factory and registry patterns.

Community
Advanced