Can I use this for languages other than Go?

While the default configuration uses Go-specific commands like 'go test' and 'go fmt', the logic and steps are designed to be adapted to any language with a standard test runner and linter.

Does this skill change the functionality of my code?

No. The primary principle of this skill is functional invariance, meaning the external behavior of your code remains identical while the internal structure is improved.

What kind of improvements does it prioritize?

It focuses on high-impact areas first, such as overly complex functions that are prone to bugs, frequently changed files, and code with poor naming or magic numbers.

How does the skill prevent breaking changes?

It follows a strict workflow: running existing tests to establish a baseline, making small incremental changes, and re-running tests after every step to ensure everything still passes.

Code Refactor & Quality

Name: Code Refactor & Quality
Author: kis9a

bykis9a

Developer Tools

Optimizes code maintainability and readability while ensuring functional consistency through automated testing and best practices.

About

The Code Refactor skill automates the systematic improvement of your codebase without altering its external behavior. It identifies opportunities to apply DRY principles, simplify complex logic, and improve naming conventions while following a strict test-driven workflow. By integrating with existing test suites and static analysis tools, it ensures that every optimization—from splitting bloated functions to removing magic numbers—is verified for safety, making it an essential tool for managing technical debt and improving long-term project health.

Key Features

Automated identification of DRY and naming improvements
Intelligent function decomposition for better modularity
Detailed refactoring reports with performance and coverage impact
Test-driven verification to guarantee functional invariance
Seamless integration with linting and formatting tools
0 GitHub stars

Use Cases

Standardizing naming conventions and error handling across a repository
Cleaning up legacy functions with high cyclomatic complexity
Simplifying complex conditional logic and removing magic numbers

About

Key Features

Automated identification of DRY and naming improvements
Intelligent function decomposition for better modularity
Detailed refactoring reports with performance and coverage impact
Test-driven verification to guarantee functional invariance
Seamless integration with linting and formatting tools
0 GitHub stars

Use Cases

Standardizing naming conventions and error handling across a repository
Cleaning up legacy functions with high cyclomatic complexity
Simplifying complex conditional logic and removing magic numbers