How does time-skipping help in Temporal testing?

Time-skipping allows you to advance the virtual clock in the WorkflowEnvironment, enabling you to test workflows that involve long timers or sleeps (like months) in just a few seconds.

What is the recommended test coverage for Temporal workflows?

Following Temporal best practices, this skill helps you target at least 80% logic coverage for both workflows and activities to ensure production reliability.

Can I use this for local development setup?

Yes, it provides guidance on setting up a local Temporal server using Docker Compose and configuring pytest for your development environment.

Does this skill support replay testing for existing workflows?

Yes, it includes specific resources for replay testing, which helps you validate that new code changes won't break the determinism of workflows already running in production.

Temporal Python Testing Suite

Name: Temporal Python Testing Suite
Author: HermeticOrmus

byHermeticOrmus

安全与测试

Streamlines the testing of Temporal workflows and activities using pytest with advanced time-skipping and mocking strategies.

关于

The Temporal Python Testing Suite is a specialized skill designed to help developers build robust, deterministic workflows by providing comprehensive testing patterns. It simplifies complex testing scenarios such as unit testing with time-skipping (running month-long workflows in seconds), integration testing with activity mocking, and replay testing to ensure production history compatibility. Whether you are setting up a local development environment with Docker or integrating automated tests into a CI/CD pipeline, this skill provides the necessary fixtures, patterns, and guidance to achieve high test coverage for backend orchestration.

主要功能

Integrated pytest fixtures for Workflow and Activity environments
Activity mocking and error injection strategies for isolated logic testing
Time-skipping capabilities for instantaneous testing of long-running workflows
Determinism validation through production history replay testing
0 GitHub stars
Pre-configured local development and CI/CD setup patterns

使用场景

Validating workflow determinism before deploying versioned updates to production
Simulating external service failures using activity mocking and error injection
Developing fast unit tests for complex business logic in Temporal workflows

关于

主要功能

Integrated pytest fixtures for Workflow and Activity environments
Activity mocking and error injection strategies for isolated logic testing
Time-skipping capabilities for instantaneous testing of long-running workflows
Determinism validation through production history replay testing
0 GitHub stars
Pre-configured local development and CI/CD setup patterns

使用场景

Validating workflow determinism before deploying versioned updates to production
Simulating external service failures using activity mocking and error injection
Developing fast unit tests for complex business logic in Temporal workflows