Does this guide include standardized testing workflows?

Yes, it outlines a 4-phase Quality Check Workflow encompassing Static Analysis, Build Verification, Testing (Unit, Integration, E2E), and a Final Quality Gate.

What core design principles does this skill enforce?

The skill enforces the Rule of Three for duplication, the Single Responsibility Principle (SRP), DRY, and Fail-Fast fallback design to ensure code remains maintainable and reliable.

How does the impact analysis process help prevent bugs?

It requires a mandatory 3-stage process—Discovery, Understanding, and Identification—to map all direct and indirect dependencies before any code is modified.

What is the 'Rule of Three' in this context?

It's a refactoring guide: the 1st time you write code you inline it, the 2nd time you plan for consolidation, and the 3rd time you must commonalize it to prevent technical debt.

AI Development Guide

Name: AI Development Guide
Author: shinpr

byshinpr

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Developer Tools

Enforces technical decision criteria, anti-pattern detection, and rigorous quality assurance workflows for high-reliability development.

About

The AI Development Guide is a comprehensive framework designed to elevate code quality and architectural integrity within AI-assisted workflows. It provides Claude with a standardized set of rules for detecting common red flags like DRY and SRP violations, implementing robust 'Fail-Fast' error handling, and applying the 'Rule of Three' for refactoring. By integrating structured impact analysis and a multi-phase quality gate, this skill ensures that every technical decision is documented, every change is validated, and technical debt is proactively managed through industry-standard best practices.

Key Features

35 GitHub stars
Standardized multi-phase quality check workflow for testing and static analysis
Mandatory 3-stage impact analysis process for all code modifications
Fail-fast fallback design principles to prevent silent error masking
Structured root cause analysis using the 5 Whys debugging technique
Automated anti-pattern detection for SRP, DRY, and code duplication

Use Cases

Systematizing the debugging process for complex, hard-to-reproduce software errors
Performing thorough impact assessments before implementing large-scale architectural changes
Conducting deep-dive refactoring sessions based on established duplication criteria

About

Key Features

35 GitHub stars
Standardized multi-phase quality check workflow for testing and static analysis
Mandatory 3-stage impact analysis process for all code modifications
Fail-fast fallback design principles to prevent silent error masking
Structured root cause analysis using the 5 Whys debugging technique
Automated anti-pattern detection for SRP, DRY, and code duplication

Use Cases

Systematizing the debugging process for complex, hard-to-reproduce software errors
Performing thorough impact assessments before implementing large-scale architectural changes
Conducting deep-dive refactoring sessions based on established duplication criteria