Does this skill help with testing external APIs?

Yes, it provides detailed patterns for mocking and patching external dependencies using unittest.mock and pytest-mock to ensure your test suite remains fast and isolated.

Which Python testing frameworks are supported?

This skill primarily focuses on pytest, the industry-standard framework, but also includes patterns for unittest.mock, Hypothesis for property-based testing, and pytest-asyncio for async operations.

Can I use this for CI/CD automation?

Absolutely. It includes configurations for test discovery, coverage thresholds, and reporting tools like pytest-cov that are essential for robust CI/CD pipelines.

How does it handle asynchronous Python code?

It includes specialized patterns for testing async functions and concurrent logic using pytest-asyncio and proper event loop management.

Python Testing Patterns

Name: Python Testing Patterns
Author: NickCrew

byNickCrew

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Security & Testing

Implements robust Python testing strategies using pytest, mocking, and property-based testing to ensure production-grade code quality.

About

The Python Testing Patterns skill provides a comprehensive framework for implementing high-quality testing across Python projects. It guides developers through the entire testing lifecycle, from basic unit test construction and fixture management to advanced techniques like mocking external dependencies, testing asynchronous operations, and implementing property-based testing with Hypothesis. By providing standardized patterns for test-driven development (TDD), integration testing, and CI/CD automation, this skill helps teams build reliable, maintainable codebases while maximizing test coverage and execution efficiency.

Key Features

Advanced mocking and patching patterns for external APIs and services
Standardized pytest configuration and project-wide fixture management
7 GitHub stars
Property-based testing integration using Hypothesis for edge-case detection
Comprehensive async testing workflows for concurrent Python applications
Automated coverage reporting and performance optimization strategies

Use Cases

Setting up a modern pytest-based testing infrastructure for new or legacy Python projects
Implementing complex integration tests for databases, APIs, and message queues
Refactoring codebases using Test-Driven Development (TDD) best practices

About

Key Features

Advanced mocking and patching patterns for external APIs and services
Standardized pytest configuration and project-wide fixture management
7 GitHub stars
Property-based testing integration using Hypothesis for edge-case detection
Comprehensive async testing workflows for concurrent Python applications
Automated coverage reporting and performance optimization strategies

Use Cases

Setting up a modern pytest-based testing infrastructure for new or legacy Python projects
Implementing complex integration tests for databases, APIs, and message queues
Refactoring codebases using Test-Driven Development (TDD) best practices