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moai-workflow-testing

Automates DDD-based workflows for testing, debugging, performance profiling, and code review with TRUST 5 standards.

Updated Nov 28, 2024
Search Tags:#workflow#testing#code review#performance#debugging#ai#ddd
One-click install
npx skills add https://github.com/desafin/Resource-Monitor-pyQT --skill moai-workflow-testing-desafin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-workflow-testing
Source: https://github.com/desafin/Resource-Monitor-pyQT/tree/main/.claude/skills/moai-workflow-testing
Command: npx skills add https://github.com/desafin/Resource-Monitor-pyQT --skill moai-workflow-testing-desafin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest, Context7 MCP, ast, subprocess, asyncio, dataclasses, pylint, flake8, bandit, mypy, rope, playwright-lighthouse, axe-playwright, mutmut, pytest-watch, cProfile, memory_profiler, psutil, radon, mccabe, and includes scripts (resource) and references (resource) and modules (resource) components.

What problem does it solve?

This Skill streamlines the entire software development lifecycle, from writing tests and debugging to optimizing performance and ensuring code quality through automated reviews.

Core Features & Use Cases

  • DDD Workflow: Automates the ANALYZE-PRESERVE-IMPROVE cycle for robust development.
  • AI Debugging: Provides intelligent error analysis and solution suggestions.
  • Performance Optimization: Detects bottlenecks and suggests AI-driven improvements.
  • Automated Code Review: Enforces quality standards using the TRUST 5 framework.
  • Use Case: A developer can initiate a full DDD cycle for a new feature, automatically generating tests, implementing the code, refactoring it safely, and ensuring it meets all quality and performance benchmarks.

Quick Start

Use the moai-workflow-testing skill to run a full DDD cycle for the 'user_authentication' feature.

Frequently Asked Questions about moai-workflow-testing

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

FAQPage Schema
How do I integrate Domain-Driven Design principles into my automated testing workflow?▼

To integrate Domain-Driven Design (DDD) into your automated testing workflow, use the ANALYZE-PRESERVE-IMPROVE cycle to generate characterization tests, refactor code safely via rope, and enforce quality benchmarks using the TRUST 5 framework.

Can I automate performance profiling and bottleneck detection for my Python code?▼

Yes, you can automate performance profiling and bottleneck detection in Python using cProfile, memory_profiler, and psutil to measure execution metrics and suggest AI-driven improvements for your application.

What is the best way to generate automated tests and catch regressions during development?▼

The best way to generate automated tests and catch regressions is combining pytest with pytest-watch for continuous execution, mutmut for mutation testing, and AI-driven test generation to cover edge cases.

How does AI debugging with Context7 patterns help resolve complex errors?▼

AI debugging with Context7 patterns helps resolve complex errors by analyzing the codebase context to provide intelligent error analysis and targeted solution suggestions during the software development lifecycle.

Do I need pylint and mypy to run automated code reviews with the TRUST 5 framework?▼

Yes, the TRUST 5 framework for automated code review enforces quality standards by leveraging linters like pylint, flake8, bandit, and mypy to analyze static code and security vulnerabilities.

What are the limitations of using DDD workflows for legacy codebases without characterization tests?▼

Without characterization tests, applying DDD workflows to legacy codebases risks regressions during the PRESERVE phase, as mutmut and pytest cannot verify existing behavior before AI-driven refactoring occurs.

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