Guide · Agent skill
How to use the Temporal Python Testing agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The temporal-python-testing skill gives an AI agent a structured set of testing patterns for Temporal workflows written in Python. It covers pytest setup, time-skipping for long-running workflows, mocking activities, and replay testing against recorded histories. This guide explains what the skill does, how to install it, how to trigger it, and what to watch for before you rely on it. It is aimed at Python developers who already build Temporal workflows and want a test suite, and at anyone debugging a failing Temporal workflow test. You do not need prior experience with agent skills to follow along.
Temporal Python Testingwshobson/agents · MIT Download the skill Quick start
- 1Download the ZIP. Download the temporal-python-testing skill as a ZIP from the item page on this directory.
- 2Unzip into a skills folder. Unzip it into ~/.claude/skills/ for all projects, or into a project's .claude/skills/ folder for one project.
- 3Or upload in the Claude apps. In the Claude apps, open Settings → Capabilities → Skills and upload the ZIP.
- 4Confirm your requirements. Make sure your project uses Python, pytest and the temporalio Python SDK. Install Docker if you want the local Temporal server setup.
- 5Ask for what you need. Describe the test task in plain language, such as asking for unit testing patterns or help debugging a failing workflow test.
- 6Review the generated tests. Run the tests in your environment and check that the assertions match your workflow's intended behaviour.
What the temporal-python-testing skill makes the agent do
Once installed, the agent can help you test Temporal workflows written in Python. The skill's SKILL.md describes testing with pytest and time-skipping, so a workflow that would run for a month can finish in seconds. It also describes mocking activities to isolate workflow logic from external services, and replay testing to check workflow code against production histories before deployment.
The skill is split into reference files that the agent loads only when relevant. Unit testing covers WorkflowEnvironment with time-skipping and ActivityEnvironment for activities. Integration testing covers mocked activities, error injection, signals and queries. Replay testing covers determinism checks and CI/CD patterns. Local setup covers Docker Compose configuration, pytest configuration and coverage tooling.
- resources/unit-testing.md: workflows with time-skipping, activities in isolation
- resources/integration-testing.md: workers with mocked activities, error injection, signal and query testing
- resources/replay-testing.md: determinism validation and replaying production histories
- resources/local-setup.md: local Temporal server with Docker Compose, pytest and coverage setup
Install the temporal-python-testing skill
Download the skill as a ZIP from the item page on this directory. Then choose one of the two install routes below.
For Claude Code, unzip the download into ~/.claude/skills/ to make the skill available to all your projects, or into a project's .claude/skills/ folder to limit it to that project. The folder should contain SKILL.md and the resources directory.
In the Claude apps, go to Settings → Capabilities → Skills and upload the ZIP.
Check the requirements before you start
The skill assumes a Python project that uses Temporal. The source lists these requirements: Python, pytest, the temporalio Python SDK, and Docker for the local Temporal server setup described in local-setup.md.
The skill does not run scripts and does not use the network itself. It provides guidance and code patterns, which the agent adapts to your project. Any tests you run still execute on your machine in your environment.
- Python
- pytest
- temporalio Python SDK
- Docker (only for the local Temporal server setup in local-setup.md)
Trigger the skill with natural requests
You do not need a special command. Describe the task in plain language and the agent will load the relevant resource. The SKILL.md file lists these example requests and the file each one maps to.
- "Show me unit testing patterns" loads resources/unit-testing.md
- "How do I mock activities?" loads resources/integration-testing.md
- "Setup local Temporal server" loads resources/local-setup.md
- "Validate determinism" loads resources/replay-testing.md
- "My Temporal workflow test is failing, help me debug it"
Example: a basic workflow test
The skill includes a starting pattern. It creates a time-skipping WorkflowEnvironment as a pytest fixture, starts a Worker with your workflow and activities on a test task queue, executes the workflow and asserts on the result. Ask the agent to adapt it to your workflow class and expected output.
For activities, the skill uses ActivityEnvironment and runs the activity directly with a test input, then checks the output.
@pytest.fixture
async def workflow_env():
env = await WorkflowEnvironment.start_time_skipping()
yield env
await env.shutdown()Choose the right test type
The skill describes three test types. Unit tests cover workflows with time-skipping and activities with ActivityEnvironment. Integration tests run workers with mocked activities. End-to-end tests run against a full Temporal server with real activities, and the skill advises using them sparingly.
The recommended approach is to write most tests as integration tests. Replay tests sit alongside these and should run for all workflow versions before deployment.
Understand coverage and replay targets
The skill suggests targeting at least 80% logic coverage for workflows and activities, and recommends that critical paths are covered by integration tests with mocked activities. These are targets from the skill's guidance, not guarantees about your code. Ask the agent to set up coverage tooling using the local-setup.md resource.
What people use it for
- Adding a pytest suite with time-skipping to a Python Temporal workflow
- Mocking activities that call external services so workflow logic can be tested alone
- Running replay tests in CI to catch non-deterministic changes before deploying a workflow update
- Setting up a local Temporal server with Docker Compose for development
- Debugging a failing Temporal workflow test by loading the relevant reference file
Frequently asked questions
How do I install the temporal-python-testing skill?
Download the skill ZIP from the item page, then unzip it into ~/.claude/skills/ or a project's .claude/skills/ folder. In the Claude apps, you can instead upload the ZIP under Settings → Capabilities → Skills.
What do I need to use this skill?
The source lists Python, pytest, the temporalio Python SDK, and Docker for the local Temporal server setup. The skill is only relevant if your project uses Temporal workflows and activities.
Is the temporal-python-testing skill free?
The item is listed under the MIT licence. Check the repository licence file for the full terms before reuse.
Does the skill run scripts or use the network?
According to the listing, it does not run scripts and does not use the network. It supplies guidance and code patterns that the agent adapts to your project.
Does this work for languages other than Python?
No. The examples and fixtures are Python-specific, and the skill applies only to Temporal workflows and activities. Teams using Temporal from other languages should look for guidance in their own SDK.
What should I do if my Temporal workflow test fails?
Describe the failure to the agent and ask for help debugging it, so it can load the relevant testing reference. Check that your activities are mocked correctly and that your worker registers the same workflows and activities the test uses.
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Our verdict
This skill suits Python developers building Temporal workflows who want a structured test suite, teams adding replay tests before deployments, and engineers debugging failing workflow tests. It is not useful for projects that do not use Temporal or for teams using Temporal SDKs in other languages. Treat its patterns as a starting point and verify the generated tests against your own workflows.
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