Temporal Python Testing logo

Temporal Python Testing

CommunityPopular
wshobson
temporal-python-testing

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

Overview

Publisherwshobson
Repositoryagents
Skill nametemporal-python-testing
Stars
39.8K
Forks
4.2K
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wshobson on GitHub. Read the source before you install it.

Installation

Install the Temporal Python Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/backend-development/skills/temporal-python-testing .claude/skills/temporal-python-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Temporal Python Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Temporal Python Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Temporal Python Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Temporal Python Testing Strategies

Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios.

When to Use This Skill

  • Unit testing workflows - Fast tests with time-skipping
  • Integration testing - Workflows with mocked activities
  • Replay testing - Validate determinism against production histories
  • Local development - Set up Temporal server and pytest
  • CI/CD integration - Automated testing pipelines
  • Coverage strategies - Achieve ≥80% test coverage

Testing Philosophy

Recommended Approach (Source: docs.temporal.io/develop/python/testing-suite):

  • Write majority as integration tests
  • Use pytest with async fixtures
  • Time-skipping enables fast feedback (month-long workflows → seconds)
  • Mock activities to isolate workflow logic
  • Validate determinism with replay testing

Three Test Types:

  1. Unit: Workflows with time-skipping, activities with ActivityEnvironment
  2. Integration: Workers with mocked activities
  3. End-to-end: Full Temporal server with real activities (use sparingly)

Available Resources

This skill provides detailed guidance through progressive disclosure. Load specific resources based on your testing needs:

Unit Testing Resources

File: resources/unit-testing.md When to load: Testing individual workflows or activities in isolation Contains:

  • WorkflowEnvironment with time-skipping
  • ActivityEnvironment for activity testing
  • Fast execution of long-running workflows
  • Manual time advancement patterns
  • pytest fixtures and patterns

Integration Testing Resources

File: resources/integration-testing.md When to load: Testing workflows with mocked external dependencies Contains:

  • Activity mocking strategies
  • Error injection patterns
  • Multi-activity workflow testing
  • Signal and query testing
  • Coverage strategies

Replay Testing Resources

File: resources/replay-testing.md When to load: Validating determinism or deploying workflow changes Contains:

  • Determinism validation
  • Production history replay
  • CI/CD integration patterns
  • Version compatibility testing

Local Development Resources

File: resources/local-setup.md When to load: Setting up development environment Contains:

  • Docker Compose configuration
  • pytest setup and configuration
  • Coverage tool integration
  • Development workflow

Quick Start Guide

Basic Workflow Test

python
import pytest
from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker

@pytest.fixture
async def workflow_env():
    env = await WorkflowEnvironment.start_time_skipping()
    yield env
    await env.shutdown()

@pytest.mark.asyncio
async def test_workflow(workflow_env):
    async with Worker(
        workflow_env.client,
        task_queue="test-queue",
        workflows=[YourWorkflow],
        activities=[your_activity],
    ):
        result = await workflow_env.client.execute_workflow(
            YourWorkflow.run,
            args,
            id="test-wf-id",
            task_queue="test-queue",
        )
        assert result == expected

Basic Activity Test

python
from temporalio.testing import ActivityEnvironment

async def test_activity():
    env = ActivityEnvironment()
    result = await env.run(your_activity, "test-input")
    assert result == expected_output

Coverage Targets

Recommended Coverage (Source: docs.temporal.io best practices):

  • Workflows: ≥80% logic coverage
  • Activities: ≥80% logic coverage
  • Integration: Critical paths with mocked activities
  • Replay: All workflow versions before deployment

Key Testing Principles

  1. Time-Skipping - Month-long workflows test in seconds
  2. Mock Activities - Isolate workflow logic from external dependencies
  3. Replay Testing - Validate determinism before deployment
  4. High Coverage - ≥80% target for production workflows
  5. Fast Feedback - Unit tests run in milliseconds

How to Use Resources

Load specific resource when needed:

  • "Show me unit testing patterns" → Load resources/unit-testing.md
  • "How do I mock activities?" → Load resources/integration-testing.md
  • "Setup local Temporal server" → Load resources/local-setup.md
  • "Validate determinism" → Load resources/replay-testing.md

Additional References

  • Python SDK Testing: docs.temporal.io/develop/python/testing-suite
  • Testing Patterns: github.com/temporalio/temporal/blob/main/docs/development/testing.md
  • Python Samples: github.com/temporalio/samples-python

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Temporal Python Testing AI skill do?

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

Why use Temporal Python Testing on TypingMind?

Because you install it once and use it with any model. Temporal Python Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Temporal Python Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/backend-development/skills/temporal-python-testing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Temporal Python Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Temporal Python Testing?

As many as you like. As long as a model supports skills, you can use Temporal Python Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Temporal Python Testing AI skill free?

Yes. It is published on GitHub by wshobson under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇