Does this skill support code coverage reporting?

Yes, it includes tools to measure coverage, identify untested code, and generate detailed HTML or terminal-based reports.

Does it work with the uv package manager?

Yes, the provided commands are optimized for use with 'uv run pytest' to ensure efficient and consistent environment management.

How does it help with test organization?

It provides best practices for structuring test directories, using conftest.py for shared fixtures, and marking tests for selective execution.

Can I run specific tests instead of the entire suite?

Absolutely. The skill supports running tests by file path, pattern matching, or focusing specifically on the last failed tests for faster debugging.

Can this skill help me write new test cases?

Yes, it includes templates and patterns for unit tests, integration tests, and parameterized tests to help you quickly expand your test suite.

Python Testing

Name: Python Testing
Author: jwplatta

byjwplatta

•

Security & Testing

Implements comprehensive testing suites using pytest, covering unit tests, integration tests, and detailed coverage reporting.

About

This skill empowers Claude to manage the entire Python testing lifecycle, from initial test creation and organization to execution and coverage analysis. It provides domain-specific expertise in pytest patterns, including the effective use of fixtures, parametrization, and directory structuring with conftest.py. By integrating coverage reporting and automated execution commands, it ensures code reliability and helps developers identify untested paths, making it an essential tool for maintaining high-quality Python software projects.

Key Features

Detailed code coverage analysis with HTML and terminal-based missing line reports.
Automated test execution and reporting using pytest and the uv package manager.
Logical test organization and structure for scalable project maintenance.
1 GitHub stars
Advanced test patterns including reusable fixtures and parameterized test cases.
Selective test execution to debug failures or isolate specific features.

Use Cases

Debugging complex test failures using verbose output and failure-specific execution commands.
Analyzing existing codebases to identify and fill gaps in test coverage.
Building a comprehensive unit and integration test suite for a new Python application.

About

Key Features

Detailed code coverage analysis with HTML and terminal-based missing line reports.
Automated test execution and reporting using pytest and the uv package manager.
Logical test organization and structure for scalable project maintenance.
1 GitHub stars
Advanced test patterns including reusable fixtures and parameterized test cases.
Selective test execution to debug failures or isolate specific features.

Use Cases

Debugging complex test failures using verbose output and failure-specific execution commands.
Analyzing existing codebases to identify and fill gaps in test coverage.
Building a comprehensive unit and integration test suite for a new Python application.