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temporal-python-testing

Test Temporal workflows with pytest, time-skipping, and replay determinism.

Updated Apr 5, 2026
Search Tags:#pytest#mocking#ci-cd#temporal#time-skipping#workflow-testing#replay-testing
One-click install
npx skills add https://github.com/Jhabbig/Habbig --skill temporal-python-testing-jhabbig
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: temporal-python-testing
Source: https://github.com/Jhabbig/Habbig/tree/main/.claude/plugins/wshobson/backend-development/skills/temporal-python-testing
Command: npx skills add https://github.com/Jhabbig/Habbig --skill temporal-python-testing-jhabbig

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you verify Temporal workflows and activities without slow, flaky, or non-deterministic tests, so you can catch logic, retry, and compatibility issues before deployment.

Core Features & Use Cases

  • Unit testing with time-skipping for fast workflow execution.
  • Integration testing with mocked activities, signals, queries, and retry paths.
  • Replay testing to validate determinism against saved production histories.
  • Local development setup for Temporal server, pytest, and coverage workflows.
  • Use it when you need to debug a failing workflow test, build CI checks, or confirm a workflow change is safe to ship.

Quick Start

Write a pytest test for my Temporal workflow that uses a time-skipping test environment, mocks external activities, and verifies determinism with replay guidance.

Frequently Asked Questions about temporal-python-testing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I test Temporal workflows in Python without non-deterministic failures?▼

Testing Temporal workflows deterministically in Python involves using time-skipping test environments and replay testing to validate workflow behavior against saved histories. This approach prevents flaky tests and catches logic issues.

What is the best way to mock activities in pytest for Temporal workflows?▼

Mocking activities in pytest for Temporal workflows requires using activity mocking within integration tests to validate signals, queries, and retry paths without hitting external systems. This ensures fast and reliable test execution.

How does replay testing work for Temporal workflow history determinism?▼

Replay testing for Temporal workflow determinism works by replaying saved production histories through your workflow code to verify compatibility. This confirms that workflow changes remain safe to ship before deployment.

Can I use pytest with Temporal time-skipping for fast workflow execution?▼

Yes, you can use pytest with Temporal time-skipping environments for fast workflow execution. This setup accelerates local developer testing by instantly advancing workflow timers instead of waiting in real time.

Why are my Temporal workflow tests failing during CI-CD pipeline execution?▼

Temporal workflow tests often fail during CI-CD pipelines due to non-deterministic code or missing activity mocks. Applying replay checks and time-skipping environments validates retry scenarios and compatibility to prevent these failures.

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