Why is replay testing important for Temporal?

Replay testing ensures that code changes haven't broken determinism, preventing 'non-deterministic' errors when existing workflows are resumed or migrated in production.

What is the recommended test coverage for Temporal?

The skill follows industry best practices, recommending at least 80% logic coverage for both workflows and activities to ensure production stability.

Can I test activities in isolation?

Yes, this skill includes patterns for using ActivityEnvironment to run and verify individual activities independently of a workflow.

What is time-skipping in Temporal testing?

Time-skipping is a feature of the Temporal test environment that allows long-running workflows—such as those waiting for months—to execute in seconds by automatically advancing the internal clock.

Does this skill support CI/CD integration?

Absolutely. It provides specific resources for setting up automated testing pipelines and replay tests within standard CI/CD environments.

Temporal Python Testing

Name: Temporal Python Testing
Author: amurata

byamurata

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Seguridad y Pruebas

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

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This skill provides a robust framework for testing Temporal workflows and activities within a Python environment. It guides developers through various testing tiers—from high-speed unit tests utilizing time-skipping to integration tests with mocked activities and production-grade replay testing to ensure determinism. By providing specialized resources for local development, CI/CD integration, and coverage strategies, it enables engineering teams to maintain reliable distributed systems with high confidence and fast feedback loops.

Características Principales

Unit testing with WorkflowEnvironment and time-skipping capabilities
Local development setup via Docker Compose and pytest configuration
Coverage strategies aimed at achieving ≥80% workflow logic coverage
Integration testing strategies with activity mocking and error injection
Replay testing to validate workflow determinism against historical data
3 GitHub stars

Casos de Uso

Verifying long-running workflow logic in seconds using time-skipping
Debugging workflow failures by replaying production execution histories
Setting up automated testing pipelines for Temporal-based backend services

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Características Principales

Unit testing with WorkflowEnvironment and time-skipping capabilities
Local development setup via Docker Compose and pytest configuration
Coverage strategies aimed at achieving ≥80% workflow logic coverage
Integration testing strategies with activity mocking and error injection
Replay testing to validate workflow determinism against historical data
3 GitHub stars

Casos de Uso

Verifying long-running workflow logic in seconds using time-skipping
Debugging workflow failures by replaying production execution histories
Setting up automated testing pipelines for Temporal-based backend services