Can it be used for languages other than JavaScript?

Yes. While the documentation provides JavaScript examples, the core checklists for logic, performance, and maintainability are language-agnostic and applicable to most programming environments.

Does it replace automated linting tools?

No, it complements tools like ESLint or Prettier by focusing on higher-level semantic logic, architectural patterns, and error handling strategies that standard linters often miss.

How does this skill integrate with Ultrawork?

It is specifically designed for the VERIFICATION phase of the Ultrawork workflow, allowing reviewer agents to assess work before marking tasks as complete.

What metrics does the scoring system use?

The skill calculates a weighted average based on Logic Clarity (25%), Error Handling (25%), Maintainability (20%), Performance (15%), and Testing (15%).

Code Quality Reviewer

Name: Code Quality Reviewer
Author: mnthe

bymnthe

•

Collaboration Tools

Conducts systematic code quality assessments for Claude Code agents, evaluating logic clarity, error handling, and maintainability.

About

The Code Quality Review skill provides a structured framework for Claude Code agents to verify implementations during the final stages of development. It offers comprehensive checklists across critical dimensions including logic readability, error resilience, code style, and performance efficiency, ensuring that AI-generated or human-written code meets production-grade standards. By utilizing standardized scoring and red-flag identification, it helps teams maintain high engineering standards while automating the verification phase of complex tasks.

Key Features

2 GitHub stars
Multi-dimensional quality checklists covering logic, style, and performance
Actionable feedback patterns for immediate code improvement
Standardized red flag identification for common code anti-patterns
Detailed evidence generation for task verification and auditing
Automated quality scoring system (0-10) for objective evaluations

Use Cases

Auditing existing codebases for technical debt and performance bottlenecks
Enforcing consistent coding standards across multi-agent development workflows
Final verification of tasks completed by AI worker agents in Ultrawork

About

Key Features

2 GitHub stars
Multi-dimensional quality checklists covering logic, style, and performance
Actionable feedback patterns for immediate code improvement
Standardized red flag identification for common code anti-patterns
Detailed evidence generation for task verification and auditing
Automated quality scoring system (0-10) for objective evaluations

Use Cases

Auditing existing codebases for technical debt and performance bottlenecks
Enforcing consistent coding standards across multi-agent development workflows
Final verification of tasks completed by AI worker agents in Ultrawork