Is this compatible with existing CI/CD pipelines?

Yes, it includes guidance for setting up Temporal test environments and Docker containers in CI/CD, ensuring your workflows are validated automatically on every pull request.

How does time-skipping work with this skill?

It utilizes the Temporal Python SDK's WorkflowEnvironment to advance internal clocks manually, allowing you to execute and validate month-long workflows in milliseconds.

Can I use this for production determinism checks?

Yes, the skill includes specialized resources for Replay Testing, which validates your code against actual production history to prevent non-deterministic errors during deployment.

Does it support activity mocking?

Absolutely. It provides specific patterns for isolating workflow logic by mocking external activity calls using standard Python mocking libraries and integrated pytest fixtures.

Temporal Python Testing

Name: Temporal Python Testing
Author: HermeticOrmus

byHermeticOrmus

Seguridad y Pruebas

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

Acerca de

This skill provides a comprehensive framework for testing Temporal workflows in Python, enabling developers to validate distributed systems logic with high confidence. It offers specialized guidance on unit testing with time-skipping—turning month-long workflows into seconds of execution—as well as integration testing with mocked activities and replay testing to ensure determinism. By providing ready-to-use patterns for local setup, pytest fixtures, and CI/CD integration, it helps development teams maintain high reliability and achieve over 80% code coverage in their Temporal applications.

Características Principales

Deterministic replay testing for production safety
Comprehensive local development and CI/CD setup guides
0 GitHub stars
Automated activity mocking and error injection patterns
Advanced time-skipping for near-instant workflow validation
Pre-configured pytest fixtures for async environments

Casos de Uso

Testing complex long-running business processes in seconds
Validating workflow determinism against production history logs
Implementing high-coverage unit tests for distributed activity logic

Acerca de

Características Principales

Deterministic replay testing for production safety
Comprehensive local development and CI/CD setup guides
0 GitHub stars
Automated activity mocking and error injection patterns
Advanced time-skipping for near-instant workflow validation
Pre-configured pytest fixtures for async environments

Casos de Uso

Testing complex long-running business processes in seconds
Validating workflow determinism against production history logs
Implementing high-coverage unit tests for distributed activity logic