Pytest Testing Guidelines

Provides pytest guidelines for naming conventions, mocking patterns, and parametrization in Python projects.

2|1|Updated Nov 27, 2025

Search Tags:#unit tests #pytest #mocking #naming-conventions #parametrization #test-templates

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npx skills add https://github.com/canvas-medical/coding-agents --skill pytest-testing-guidelines

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Skill: Pytest Testing Guidelines
Source: https://github.com/canvas-medical/coding-agents/tree/main/pytest-forge/skills/pytest-guidelines
Command: npx skills add https://github.com/canvas-medical/coding-agents --skill pytest-testing-guidelines

SYSTEM DOCUMENTATION & REQUIREMENTS

## What problem does it solve? Pytest Testing Guidelines provide a structured framework to write consistent, robust unit tests in Python, emphasizing naming conventions, mock strategies, parametrization, and comprehensive coverage.

## Core Features & Use Cases

Standardized test naming conventions and project structure that mirror source files.
Clear guidance on mocking with side_effect, mock_calls verification, and avoiding brittle assertions.
Practical parametrization patterns and real-world use cases for small utilities up to large codebases.

### Quick Start Begin by reading these guidelines, then apply the templates to your tests:

name tests after the corresponding source methods (e.g., test_my_feature)
mock external dependencies with patch or patch.object
verify mock interactions using a single object-level mock_calls assertion

Frequently Asked Questions about Pytest Testing Guidelines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I structure pytest unit tests to match my source files?▼

To structure pytest unit tests effectively, name tests after corresponding source methods like test_my_feature. This standardized naming convention mirrors your source files, ensuring robust and consistent test organization across Python projects.

What is the best way to mock external dependencies in pytest?▼

The best way to mock external dependencies in pytest is by using patch or patch.object. Enforce the use of side_effect for mocks and verify interactions through a single object-level mock_calls assertion to avoid brittle test assertions.

How do I use parametrization to improve pytest coverage?▼

You use parametrization to improve pytest coverage by applying practical parametrization patterns across real-world use cases. This approach drives comprehensive 100% coverage by allowing a single test definition to execute multiple input scenarios.

Can I use these pytest guidelines for large codebases?▼

Yes, you can use these pytest guidelines for large codebases. The framework provides practical parametrization patterns and real-world use cases specifically designed to scale from small utilities up to large Python codebases.

Why should I verify mock_calls at the object level instead of individual assertions?▼

You should verify mock_calls at the object level to avoid brittle assertions and ensure strict test organization. This method enforces robust mock interaction verification by checking all calls made to a mock object in a single assertion.