What is the Scientific Python Testing skill?

It is a specialized capability that enables Claude to write, organize, and optimize tests for numerical and research-oriented Python code using pytest best practices.

Can it help set up automated testing?

Yes, it provides templates for GitHub Actions and pyproject.toml configurations to automate your testing and coverage reporting pipelines.

How does this skill handle floating-point comparisons?

The skill prioritizes robust comparisons using pytest.approx and assert_allclose to prevent false negatives caused by standard floating-point precision issues.

Does it support NumPy and SciPy specifically?

Yes, it includes specific patterns for testing NumPy arrays and scientific data structures using community-standard utilities like numpy.testing.

Does it follow official community guidelines?

Absolutely. It is aligned with the Scientific Python Community guidelines for package development, organization, and testing principles.

Scientific Python Testing

Name: Scientific Python Testing
Author: robertpelloni

byrobertpelloni

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Security & Testing

Writes and organizes robust test suites for scientific Python packages using pytest and community best practices.

About

This skill provides specialized guidance for implementing modern testing patterns tailored to the scientific computing ecosystem. It enables Claude to create comprehensive unit, integration, and end-to-end tests for libraries utilizing NumPy, SciPy, and Pandas, ensuring numerical accuracy through tools like pytest.approx and numpy.testing. By following the Scientific Python community's 'outside-in' testing philosophy, it helps developers build maintainable, documented, and reliable research software with standardized configurations for code coverage and continuous integration.

Key Features

Scientific pytest configuration following community standards
Automated fixture and parametrization setup for complex research datasets
Performance markers and coverage reporting for scientific simulations
3 GitHub stars
Numerical accuracy validation using pytest.approx and numpy.testing
Continuous integration workflow generation for GitHub Actions

Use Cases

Implementing property-based testing for high-stakes data analysis pipelines
Validating the correctness of complex numerical algorithms and simulations
Migrating legacy research scripts into standardized, testable Python packages

About

Key Features

Scientific pytest configuration following community standards
Automated fixture and parametrization setup for complex research datasets
Performance markers and coverage reporting for scientific simulations
3 GitHub stars
Numerical accuracy validation using pytest.approx and numpy.testing
Continuous integration workflow generation for GitHub Actions

Use Cases

Implementing property-based testing for high-stakes data analysis pipelines
Validating the correctness of complex numerical algorithms and simulations
Migrating legacy research scripts into standardized, testable Python packages