Can I use this for testing FastAPI or Flask applications?

Yes, it includes specific patterns for testing web APIs, including mocking HTTP requests and setting up fixtures for integration test environments.

Does this skill support frameworks other than pytest?

While it focuses heavily on the pytest ecosystem for its power and flexibility, the skill also incorporates standard library tools like unittest.mock for versatile testing strategies across any Python project.

How does this skill handle asynchronous Python code?

The skill provides dedicated patterns for using pytest-asyncio, allowing you to effectively test coroutines, awaitables, and concurrent operations without race conditions.

Does it help with code coverage and quality metrics?

Yes, it includes guidance on measuring code coverage to identify untested paths and ensures that your tests focus on meaningful logic rather than just arbitrary percentages.

Python Testing Patterns

Name: Python Testing Patterns
Author: yusoofsh

byyusoofsh

Seguridad y Pruebas

Implements comprehensive Python testing strategies using pytest, fixtures, and mocking to ensure code reliability and maintainability.

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Python Testing Patterns is a specialized skill for Claude Code designed to help developers architect and write robust test suites. It provides deep expertise in the pytest ecosystem, covering everything from basic unit tests and the Arrange-Act-Assert (AAA) pattern to advanced concepts like asynchronous testing, dependency mocking with unittest.mock, and environment manipulation. Whether you are setting up a new test infrastructure from scratch, implementing Test-Driven Development (TDD), or debugging complex failing tests in a CI/CD pipeline, this skill provides the patterns and best practices needed to ensure your Python applications are thoroughly validated.

Características Principales

Pytest-driven test suite architecture and configuration
Mocking external dependencies and API responses using unittest.mock
Asynchronous and concurrent operation testing patterns
0 GitHub stars
Parameterized testing for comprehensive edge-case validation
Advanced fixture management with session and module scoping

Casos de Uso

Establishing a Test-Driven Development (TDD) workflow for new Python features
Mocking third-party service responses and database connections for isolated testing
Implementing complex integration tests for asynchronous APIs and microservices

Acerca de

Características Principales

Pytest-driven test suite architecture and configuration
Mocking external dependencies and API responses using unittest.mock
Asynchronous and concurrent operation testing patterns
0 GitHub stars
Parameterized testing for comprehensive edge-case validation
Advanced fixture management with session and module scoping

Casos de Uso

Establishing a Test-Driven Development (TDD) workflow for new Python features
Mocking third-party service responses and database connections for isolated testing
Implementing complex integration tests for asynchronous APIs and microservices