Is parallel test execution supported?

Yes, if the pytest-xdist plugin is available, the skill can run tests in parallel using the '-n auto' configuration to speed up large suites.

Does this skill handle virtual environment activation?

Yes, Pytest Runner automatically checks for and attempts to activate common virtual environments like 'venv' or '.venv' before executing any tests.

How does it handle test failures?

The skill implements a recovery protocol that captures output, diagnoses the specific error type—such as ImportErrors or FixtureErrors—and provides a failure analysis report.

Does it support code coverage reporting?

Yes, it includes built-in workflows for generating coverage reports, including terminal summaries and HTML exports, using the pytest-cov plugin.

Can I run specific tests based on markers?

Absolutely. The skill supports executing specific subsets of tests using pytest markers or name-based keyword filtering to save time during development.

Pytest Runner

Name: Pytest Runner
Author: jmagly

byjmagly

•

Security & Testing

Executes and manages Python test suites with automated configuration, coverage reporting, and failure analysis.

About

Pytest Runner is a specialized Claude Code skill designed to automate the lifecycle of Python testing within agentic workflows. It ensures environmental integrity by verifying virtual environments and dependencies before execution, supporting advanced features like parallel execution with xdist, code coverage reporting, and targeted testing via markers. By providing structured failure analysis and recovery protocols, this skill reduces manual debugging time and allows autonomous agents to maintain high code quality standards through rigorous, repeatable test cycles.

Key Features

Automated virtual environment and dependency verification
Structured failure diagnosis and recovery protocols
Granular test filtering using markers and name patterns
Comprehensive test discovery and collection analysis
Integrated code coverage reporting and parallel execution
67 GitHub stars

Use Cases

Running unit and integration tests during autonomous coding sessions
Generating detailed coverage reports to identify untested application logic
Debugging failed suites with automated failure categorization and state saving

About

Key Features

Automated virtual environment and dependency verification
Structured failure diagnosis and recovery protocols
Granular test filtering using markers and name patterns
Comprehensive test discovery and collection analysis
Integrated code coverage reporting and parallel execution
67 GitHub stars

Use Cases

Running unit and integration tests during autonomous coding sessions
Generating detailed coverage reports to identify untested application logic
Debugging failed suites with automated failure categorization and state saving