What is the recommended test coverage for Temporal workflows?

Best practices suggest achieving at least 80% logic coverage for both workflows and activities to ensure production reliability.

Does this support local development environments?

Yes, it includes guidance for setting up a local Temporal server using Docker Compose and configuring pytest for local execution.

Can I test activities in isolation?

Yes, this skill provides patterns using the ActivityEnvironment to test individual activity logic without running a full workflow.

What is time-skipping in Temporal testing?

Time-skipping is a feature that allows you to test workflows containing long sleeps or timers in seconds by automatically advancing the internal test clock.

Why is replay testing necessary for Temporal?

Replay testing ensures that code updates remain compatible with existing workflow histories, preventing non-determinism errors in production.

Temporal Python Testing

Name: Temporal Python Testing
Author: HermeticOrmus

byHermeticOrmus

安全与测试

Implements comprehensive testing suites for Temporal workflows using pytest, time-skipping, and determinism validation.

关于

This skill provides a complete framework for testing Temporal Python applications, enabling developers to build reliable, deterministic workflows. It covers the full testing lifecycle, from unit testing with time-skipping (allowing month-long workflows to run in seconds) to integration testing with mocked activities and replay testing against production histories. By following these patterns, developers can ensure their distributed systems are resilient, maintain high test coverage, and integrate seamlessly into CI/CD pipelines with local development support via Docker.

主要功能

Standardized pytest fixtures for Workflow and Activity environments
0 GitHub stars
Activity mocking strategies to isolate workflow logic
Time-skipping for fast execution of long-running workflows
Replay testing to validate workflow determinism
Local development setup with Docker Compose and coverage tools

使用场景

Validating workflow changes against production histories to prevent non-determinism
Setting up automated CI/CD pipelines for complex backend orchestrations
Testing long-running business processes without waiting for real-time delays

关于

主要功能

Standardized pytest fixtures for Workflow and Activity environments
0 GitHub stars
Activity mocking strategies to isolate workflow logic
Time-skipping for fast execution of long-running workflows
Replay testing to validate workflow determinism
Local development setup with Docker Compose and coverage tools

使用场景

Validating workflow changes against production histories to prevent non-determinism
Setting up automated CI/CD pipelines for complex backend orchestrations
Testing long-running business processes without waiting for real-time delays