Is the quality check workflow language-specific?

No, it follows a language-agnostic four-phase workflow including Static Analysis, Build Verification, Multi-level Testing (Unit, Integration, E2E), and a final Quality Gate.

Does this skill help with debugging complex errors?

Yes, it utilizes structured techniques like the '5 Whys' root cause analysis, first/last stack trace focus, and the creation of minimal reproduction code to isolate issues.

How does the mandatory impact analysis work?

Before any implementation, the skill performs a sequential three-stage process: Discovery of all affected references, Understanding the dependency flow, and Producing a structured Risk Assessment report.

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

It is a criteria for code duplication: implementation is kept inline the 1st time, a pattern is noted the 2nd time, and commonalization/refactoring is mandated on the 3rd occurrence.

What technical anti-patterns does this skill detect?

It identifies 'red flag' patterns such as violating the Rule of Three, mixing responsibilities in a single file, bypassing type systems, and implementing symptomatic fixes instead of solving root causes.

AI Development Playbook

Name: AI Development Playbook
Author: Tahir-yamin

byTahir-yamin

•

Herramientas para Desarrolladores

Enforces professional engineering standards, anti-pattern detection, and rigorous quality assurance workflows during AI-assisted development.

Acerca de

This skill equips Claude with a comprehensive engineering playbook designed to minimize technical debt and maximize code reliability. It provides a structured framework for technical decision-making, utilizing industry-standard principles like the Rule of Three and Single Responsibility, alongside specialized fail-fast fallback designs and root-cause analysis techniques. By integrating a multi-phase quality check workflow—from static analysis to impact evaluation—it ensures that every code modification is intentional, well-tested, and architecturally sound, making it indispensable for full-stack, DevOps, and AI engineering tasks.

Características Principales

Mandatory 3-stage impact analysis (Discovery, Understanding, Identification)
Multi-phase quality gate workflow spanning static analysis, build, and E2E testing
Fail-fast fallback design principles to prevent error masking
Structured debugging using 5 Whys and minimal reproduction patterns
3 GitHub stars
Automated anti-pattern detection for DRY, SRP, and code smell identification

Casos de Uso

Performing deep architectural reviews and refactoring complex legacy codebases
Standardizing debugging procedures and root-cause analysis across team projects
Executing rigorous pre-deployment quality checks and dependency impact assessments

Acerca de

Características Principales

Mandatory 3-stage impact analysis (Discovery, Understanding, Identification)
Multi-phase quality gate workflow spanning static analysis, build, and E2E testing
Fail-fast fallback design principles to prevent error masking
Structured debugging using 5 Whys and minimal reproduction patterns
3 GitHub stars
Automated anti-pattern detection for DRY, SRP, and code smell identification

Casos de Uso

Performing deep architectural reviews and refactoring complex legacy codebases
Standardizing debugging procedures and root-cause analysis across team projects
Executing rigorous pre-deployment quality checks and dependency impact assessments