Will it automatically change my test files?

No, it provides evidence-based recommendations and suggested code snippets, allowing you to review and apply changes safely with a focus on backward compatibility.

Can it help with CI/CD setup?

Yes, it analyzes your current test configuration to suggest optimizations and missing plugins that improve test reporting and reliability in continuous integration environments.

Does it support async testing?

Yes, it specifically looks for async code in your project and suggests appropriate tools like pytest-asyncio if tests are missing for those specific components.

How does Python Expert Tester analyze my code?

It uses file system tools to examine configuration files like pyproject.toml and scans your existing test files for patterns, naming conventions, and coverage gaps to provide data-driven feedback.

Python Expert Tester

Name: Python Expert Tester
Author: StrayDragon

byStrayDragon

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보안 및 테스팅

Analyzes your Python project's existing tests and configurations to provide evidence-based recommendations for better coverage, structure, and performance.

소개

This skill transforms Claude into a specialized Python QA architect that prioritizes evidence over generic advice. By scanning your project's configuration files like pyproject.toml and existing test suites, it identifies specific gaps such as missing async tests, non-descriptive naming patterns, or low coverage in critical functions. It is particularly useful for developers looking to modernize legacy test suites, optimize configuration for CI/CD, or implement advanced testing patterns like integration testing without breaking existing workflows.

주요 기능

Automated analysis of pytest, unittest, and coverage configurations
Intelligent dependency recommendations for testing plugins
Detection of untested async code and external dependencies
Quality scoring based on naming clarity, assertion depth, and documentation
Evidence-based test naming and structural refactoring suggestions
1 GitHub stars

사용 사례

Improving test coverage for mature Python projects with legacy code
Optimizing test suite structure and performance for faster CI/CD pipelines
Setting up modern testing configurations for new Python projects

소개

주요 기능

Automated analysis of pytest, unittest, and coverage configurations
Intelligent dependency recommendations for testing plugins
Detection of untested async code and external dependencies
Quality scoring based on naming clarity, assertion depth, and documentation
Evidence-based test naming and structural refactoring suggestions
1 GitHub stars

사용 사례

Improving test coverage for mature Python projects with legacy code
Optimizing test suite structure and performance for faster CI/CD pipelines
Setting up modern testing configurations for new Python projects