Which linter does this Claude Code skill use?

This skill utilizes ruff, an extremely fast Python linter and code formatter written in Rust, known for being significantly faster than alternative tools.

How do I start a linting check?

You can trigger the skill by asking Claude to 'lint', 'check code quality', 'style check', or specifically mentioning 'ruff' in your prompt.

What are the requirements for using this skill?

This skill requires Python and the ruff package to be installed in your local development environment where Claude Code is running.

Can this skill automatically fix my Python code?

Yes, you can trigger the skill to automatically fix issues by asking Claude to 'fix' style issues or by using the ruff --fix command through the skill.

Python Linting & Code Quality

Name: Python Linting & Code Quality
Author: robertpelloni

byrobertpelloni

•

개발자 도구

Optimizes Python codebases by identifying and fixing style issues and errors using the high-performance ruff linter.

소개

This skill integrates the lightning-fast ruff linter into the Claude Code environment, enabling automated code quality checks and style enforcement for Python projects. It allows users to scan for common programming errors, unused imports, and PEP 8 violations while providing automated fixes to maintain a clean, professional, and performant codebase. Whether you are preparing for a code review or cleaning up a legacy repository, this skill streamlines the process of ensuring your Python code meets modern standards.

주요 기능

High-speed Python linting powered by ruff
Customizable rule sets to ignore specific violations
Standardized output for easy debugging and review
3 GitHub stars
Automated fixing of common style and logic issues
Granular control over error and warning selection

사용 사례

Ensuring production-ready code by catching syntax and logic errors
Standardizing code style across a large Python project
Identifying and removing unused imports or variables automatically

소개

주요 기능

High-speed Python linting powered by ruff
Customizable rule sets to ignore specific violations
Standardized output for easy debugging and review
3 GitHub stars
Automated fixing of common style and logic issues
Granular control over error and warning selection

사용 사례

Ensuring production-ready code by catching syntax and logic errors
Standardizing code style across a large Python project
Identifying and removing unused imports or variables automatically