What is replay testing in the context of Temporal?

Replay testing executes a workflow using a saved history from a previous real execution to ensure that current code changes haven't introduced non-deterministic behavior.

How does time-skipping work in Temporal Python tests?

Time-skipping uses a specialized WorkflowEnvironment that allows the test clock to advance instantly to the next scheduled event or timer, completing long-running tasks in milliseconds.

What is the recommended test coverage for Temporal workflows?

For production-grade systems, Temporal recommends at least 80% logic coverage for workflows and activities, complemented by replay tests for all active versions.

Should I mock every activity in my integration tests?

It is recommended to mock external dependencies and heavy activities to isolate workflow logic, though keeping a small set of real activities is useful for end-to-end verification.

Temporal Python Workflow Testing

Name: Temporal Python Workflow Testing
Author: drgaciw

bydrgaciw

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Seguridad y Pruebas

Automates and optimizes Temporal workflow testing using pytest, time-skipping, and advanced activity mocking strategies.

This skill provides a comprehensive framework for testing Temporal workflows in Python, enabling developers to validate complex business logic without waiting for real-time execution. By leveraging advanced time-skipping features, it transforms month-long workflows into tests that run in seconds. It guides users through creating robust unit and integration tests, implementing activity mocking, and performing replay testing to ensure determinism and prevent production failures. Whether you are setting up a local development environment with Docker or integrating automated tests into a CI/CD pipeline, this skill ensures your Temporal applications meet high-reliability standards with industry-standard coverage.

Características Principales

01Automated coverage strategies for workflows and activities

02Deterministic replay testing to prevent production breakages

03Standardized pytest fixtures for async workflow environments

040 GitHub stars

05Comprehensive activity mocking and dependency isolation patterns

06Advanced time-skipping for near-instant workflow validation

Casos de Uso

01Validating workflow determinism against production history before deployment

02Unit testing long-running workflows with manual time advancement

03Setting up local Temporal development environments with Docker and pytest

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add drgaciw/academic-compliance-hub-glm temporal-python-testing

For use in Claude.ai and ChatGPT

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