AI Development Engineering Guide

About

The AI Development Engineering Guide skill provides a comprehensive framework for making sound technical decisions and maintaining code integrity using proven engineering principles. It integrates sophisticated anti-pattern detection, the 'Rule of Three' for refactoring, fail-fast fallback designs, and a mandatory three-stage impact analysis. By guiding users through structured debugging techniques like the '5 Whys' and multi-phase quality assurance workflows, it transforms AI-assisted coding from simple generation into professional-grade software engineering.

Key Features

• Implementation of the 'Rule of Three' criteria for intelligent code commonalization
• 3 GitHub stars
• Detection of architectural and code quality anti-patterns like SRP and DRY violations
• Structured '5 Whys' root cause analysis and advanced debugging procedures
• Multi-phase quality assurance workflow covering static analysis, builds, and testing
• Mandatory three-stage impact analysis (Discovery, Understanding, Identification) for modifications

Use Cases

• Conducting comprehensive code reviews to identify technical debt and hidden anti-patterns
• Planning complex architectural changes with a structured impact analysis report
• Debugging persistent system errors using systematic root cause analysis and minimal reproduction code