How do I mock activities in Temporal Python?

You can use the Integration Testing resources provided by this skill to implement activity mocking patterns that isolate workflow logic from external API calls or database operations.

Why is replay testing necessary for Temporal?

Replay testing ensures that workflow code remains deterministic. If a workflow's logic changes in a way that alters its execution history, it can cause non-deterministic errors in production; replay testing catches these before deployment.

What is time-skipping in Temporal testing?

Time-skipping is a feature of the Temporal Testing SDK that allows you to run workflows that would normally take days or months in just a few seconds by advancing a virtual clock.

Can I use this skill with my existing pytest suite?

Yes, this skill provides specific async fixtures and patterns designed to integrate seamlessly with pytest and the temporalio library.

Temporal Python Testing

Name: Temporal Python Testing
Author: ccf

byccf

セキュリティとテスト

Facilitates comprehensive testing of Temporal workflows using pytest, time-skipping environments, and deterministic replay strategies.

概要

This skill provides specialized guidance for testing Temporal workflows within the Python SDK environment. It simplifies the complexity of testing distributed, long-running processes by implementing time-skipping for fast feedback, mocking activities to isolate workflow logic, and using replay testing to ensure determinism before production deployment. Whether you are setting up local development with Docker or building automated CI/CD pipelines, this skill ensures your Temporal-based backend services meet high reliability and coverage standards through progressive disclosure of unit, integration, and replay testing resources.

主な機能

Standardized pytest fixtures for Workflow and Activity environments
Local development setup with Docker Compose and coverage tools
Advanced time-skipping for testing month-long workflows in seconds
Comprehensive activity mocking and error injection strategies
0 GitHub stars
Determinism validation via production history replay testing

ユースケース

Validating workflow determinism before deploying code changes
Setting up localized Temporal testing environments for Python developers
Building fast unit tests for complex, long-running business logic

概要

主な機能

Standardized pytest fixtures for Workflow and Activity environments
Local development setup with Docker Compose and coverage tools
Advanced time-skipping for testing month-long workflows in seconds
Comprehensive activity mocking and error injection strategies
0 GitHub stars
Determinism validation via production history replay testing

ユースケース

Validating workflow determinism before deploying code changes
Setting up localized Temporal testing environments for Python developers
Building fast unit tests for complex, long-running business logic