moai-workflow-ddd

Name: moai-workflow-ddd
Availability: InStock
Author: soo-kate-yeon

Refactor legacy code with characterization tests and AST-grep transformations.

Updated Nov 20, 2025

Search Tags:#code quality #refactoring #ddd #technical debt #ast-grep #behavior preservation

Ready to run

npx skills add https://github.com/soo-kate-yeon/shadowoo --skill moai-workflow-ddd-soo-kate-yeon

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: moai-workflow-ddd
Source: https://github.com/soo-kate-yeon/shadowoo/tree/main/.agents/skills/moai-workflow-ddd
Command: npx skills add https://github.com/soo-kate-yeon/shadowoo --skill moai-workflow-ddd-soo-kate-yeon

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables developers to refactor existing codebases safely, ensuring that the observable behavior of the code remains unchanged throughout the process, thereby reducing technical debt and improving code structure.

Core Features & Use Cases

Behavior Preservation: Guarantees that code modifications do not alter existing functionality through characterization tests and AST-based analysis.
Incremental Refactoring: Facilitates safe, step-by-step code improvements using tools like AST-grep.
Use Case: You have a large, legacy Python application with minimal test coverage. You need to improve its modularity and reduce coupling without introducing regressions. This Skill will help you analyze the code, create safety nets with characterization tests, and then incrementally refactor it.

Quick Start

Use the moai-workflow-ddd skill to refactor the legacy_module.py file by extracting a method named process_data.

Frequently Asked Questions about moai-workflow-ddd

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

FAQPage Schema

How do I refactor legacy code without breaking existing functionality?▼

To safely refactor legacy code without breaking functionality, use a characterization-test-first approach to capture existing behavior. This Skill applies incremental AST-grep transformations to ensure behavior preservation while reducing technical debt.

What is AST-grep and how does it help with code modernization?▼

AST-grep analyzes code structure for structural improvements and code modernization without functional changes. It identifies domain boundaries and applies incremental transformations to safely reduce technical debt in legacy systems.

How do I reduce technical debt in a legacy Python application with minimal test coverage?▼

To reduce technical debt in a legacy application with minimal test coverage, you first create safety nets using characterization tests. Then, you incrementally refactor the code to improve modularity and reduce coupling without introducing regressions.

Can I use incremental refactoring for API migration without functional changes?▼

Yes, you can use incremental refactoring for API migration without functional changes. By analyzing code structure and applying AST-grep transformations, this approach addresses API migration scenarios while strictly ensuring behavior preservation.

When should I use characterization tests before refactoring?▼

You should use characterization tests before refactoring when a legacy system lacks sufficient test coverage and you need to guarantee behavior preservation. This approach captures the current observable behavior to prevent regressions during structural improvements.

What is the best way to identify domain boundaries in a legacy codebase?▼

The best way to identify domain boundaries in a legacy codebase is by analyzing the code structure to map dependencies. This Skill helps pinpoint domain boundaries to safely apply incremental transformations for improved maintainability.