temporal-python-testing

Overview of temporal-python-testing skill

What the temporal-python-testing skill does

The temporal-python-testing skill helps you design and run reliable tests for Temporal Python workflows, not just write generic pytest examples. It is aimed at teams building workflow-heavy systems who need fast feedback, safe refactors, and confidence that workflow code stays deterministic.

Who should install it

This skill is a strong fit for:

• Python developers already using Temporal
• teams adding test coverage to workflow code
• engineers debugging flaky Temporal tests
• reviewers preparing workflow changes for deployment
• anyone who needs temporal-python-testing for Test Automation rather than ad hoc prompt advice

It is less useful if you are still choosing a workflow engine, or if your project does not use the Temporal Python SDK.

The real job to be done

Most users do not just want “a test.” They want to answer practical questions quickly:

• How do I test workflow logic without waiting on real time?
• When should I mock activities versus run a fuller worker test?
• How do I check determinism before deploying a workflow change?
• What local setup do I need so tests run consistently in development and CI?

The temporal-python-testing skill is valuable because it organizes those decisions into test types: unit testing with time-skipping, integration testing with mocked activities, replay testing for compatibility, and local setup guidance.

What makes it different from a generic Temporal prompt

A normal prompt can produce sample code, but this skill gives a clearer testing strategy:

• it centers Temporal-specific test boundaries
• it pushes integration tests as the default workhorse
• it includes replay testing, which many generic answers omit
• it points you to focused resource files instead of one monolithic doc

That makes it more useful for install decisions and for teams trying to standardize how they test workflow code.

What to read before deciding

If you are evaluating temporal-python-testing, inspect these files first:

1. SKILL.md
2. resources/unit-testing.md
3. resources/integration-testing.md
4. resources/replay-testing.md
5. resources/local-setup.md

That reading order mirrors the most common adoption path: fast local tests first, then orchestration tests, then deployment safety.

How to Use temporal-python-testing skill

How to install temporal-python-testing

Install from the wshobson/agents repository:

npx skills add https://github.com/wshobson/agents --skill temporal-python-testing

Because the repository stores this under plugins/backend-development/skills/temporal-python-testing, confirm your tool has access to that repo path after installation.

Best starting workflow for first-time users

For a first pass, do not read everything. Use this sequence:

1. Read SKILL.md for scope and testing philosophy.
2. Open resources/unit-testing.md if your immediate goal is fast workflow tests.
3. Open resources/integration-testing.md if you need mocked activities and worker-based tests.
4. Open resources/replay-testing.md before changing workflow code already seen by running executions.
5. Open resources/local-setup.md if your blocker is environment setup rather than test design.

This path reduces the usual “too many Temporal testing options” problem.

What input the skill needs from you

The temporal-python-testing usage quality depends heavily on the details you provide. Include:

• workflow class names
• activity names and side effects
• whether the test is unit, integration, or replay
• current stack: pytest, temporalio, Docker, local Temporal server
• failure mode: timeout, nondeterminism, mock setup, worker registration, flaky assertions
• desired confidence level: local dev, CI, or pre-deploy compatibility check

Without that, the output tends to stay too generic.

Turn a rough goal into a strong prompt

Weak prompt:

• “Help me test a Temporal workflow in Python.”

Better prompt:

• “Use the temporal-python-testing skill to propose pytest tests for a Temporal Python workflow that waits on timers, calls two activities, and must stay deterministic across deployments. I want a fast local test, an integration test with mocked activities, and guidance on replay testing before release.”

Best prompt:

• “Use the temporal-python-testing skill. I have OrderWorkflow.run(order_id) that sleeps for retries, calls charge_card and send_receipt, and currently fails in CI. Generate a test plan using pytest async fixtures, WorkflowEnvironment.start_time_skipping(), mocked activity patterns where appropriate, and a replay testing step for deployment safety. Explain what should be unit tested versus integration tested.”

The stronger versions produce materially better output because they force the skill into the right test mode.

Core usage patterns the skill is built for

The repository evidence shows four practical lanes:

Unit testing

Use when you need fast feedback on workflow behavior, timers, and branching logic. The skill points to WorkflowEnvironment.start_time_skipping() so long delays complete instantly.

Integration testing

Use when you want to exercise worker registration and workflow orchestration while mocking external activity behavior. This is the recommended default for most workflow logic.

Replay testing

Use before shipping workflow code changes that may affect already-running executions. This is the highest-value part for production safety.

Local setup

Use when your real blocker is getting Temporal server, UI, and pytest environment working consistently.

What the skill implicitly recommends

The temporal-python-testing guide is not neutral on test strategy. It favors:

• majority integration tests
• unit tests for isolated workflow behavior and activity logic
• end-to-end tests used sparingly
• replay tests for backward compatibility and determinism checks

That bias is useful. It keeps teams from overinvesting in slow, fragile end-to-end suites.

Practical install context and dependencies

The skill itself is documentation-oriented, but it assumes your project can support:

• Python project with pytest
• Temporal Python SDK usage
• async test execution
• worker setup in tests
• optional Docker-based local Temporal stack for development or CI

From the resource files, local setup commonly involves Docker Compose with Temporal, Postgres, and Temporal UI. If your team cannot run Docker locally or in CI, decide that early because it changes how much of the skill you can adopt directly.

Example outcomes you can ask for

Good temporal-python-testing usage requests include:

• “Generate a pytest fixture for time-skipping workflow tests.”
• “Show how to mock activities in a worker-based integration test.”
• “Design replay tests for multiple stored workflow histories.”
• “Recommend a split between unit, integration, and end-to-end tests for this workflow.”
• “Help debug a nondeterminism failure after refactoring a workflow.”

These are all better than asking for “test examples” in the abstract.

Tips that improve output quality immediately

• Name the workflow entrypoint you want tested.
• State whether activities should be mocked or real.
• Mention timers, retries, signals, or long-running waits.
• Say whether the workflow already has production history.
• Include current error output if replay or worker startup is failing.

Temporal testing problems are usually about boundaries, not syntax. The more clearly you describe the boundary, the more useful the skill becomes.

Where this skill saves the most time

The biggest value is not boilerplate generation. It is helping you avoid common wrong turns:

• writing only end-to-end tests
• failing to use time-skipping for timer-heavy workflows
• mocking too much or too little
• skipping replay testing before workflow changes
• mixing local environment problems with test design problems

If that sounds like your team’s current pain, temporal-python-testing install is likely worthwhile.

temporal-python-testing skill FAQ

Is temporal-python-testing good for beginners?

Yes, if you already know basic pytest and basic Temporal concepts. No, if you are brand new to both. The skill assumes you understand workflows, activities, workers, and async Python well enough to place tests at the right level.

Is this better than asking an LLM for Temporal test code directly?

Usually yes for real projects. Generic prompts often miss Temporal-specific concerns like determinism, replay validation, and time-skipping. The temporal-python-testing skill is better when correctness matters more than quick sample code.

Does it help with replay testing specifically?

Yes. That is one of the strongest reasons to use temporal-python-testing. The repository includes a dedicated resources/replay-testing.md focused on validating workflow changes against recorded event histories.

When should I not use temporal-python-testing?

Skip it if:

• you are not using Temporal Python
• you only need a trivial pytest refresher
• your problem is general mocking, not workflow behavior
• you need production architecture guidance more than testing guidance

It is specialized, which is a strength only when your problem matches.

Does it cover local Temporal setup?

Yes. resources/local-setup.md includes Docker Compose based setup patterns for a local Temporal server, Postgres, and UI. That matters if your tests need a fuller development environment.

Is it mainly for unit tests?

No. The source material explicitly frames integration tests as the main testing approach, with unit tests and end-to-end tests used more selectively. If your team only wants isolated unit tests, you will use only part of the skill.

Can temporal-python-testing help in CI?

Yes, especially for:

• automated workflow test suites
• coverage-oriented testing strategy
• replay checks before deployment
• consistent environment setup across machines

The skill does not ship CI scripts, but it gives the testing patterns you would operationalize in CI pipelines.

How to Improve temporal-python-testing skill

Start with the exact test type you need

The fastest way to improve results from temporal-python-testing is to say which lane you are in:

• unit test
• integration test
• replay test
• local setup/debugging

If you do not choose, the response may blend patterns and create extra work.

Provide workflow-specific details, not just architecture summaries

Bad input:

• “We have a Temporal-based order system.”

Better input:

• “OrderWorkflow waits for payment confirmation, retries every hour, calls charge_card, and emits a receipt activity. We need tests for timeout handling and replay safety after refactoring retry logic.”

This changes the quality of the answer because timer behavior, activity orchestration, and compatibility concerns are all explicit.

Tell the skill what is already failing

Common failure modes where this skill can help more precisely:

• workflow nondeterminism after code changes
• worker not registering the expected activity or workflow
• mocked activity assertions not firing
• long-running timers making tests slow
• local Temporal environment not starting cleanly
• confusion about what should be unit versus integration tested

Lead with the failure, not just the desired end state.

Use the resource files selectively

A common mistake is treating the whole skill as one doc. Better approach:

• use resources/unit-testing.md for time-skipping fixtures and isolated behavior
• use resources/integration-testing.md for mock-driven orchestration tests
• use resources/replay-testing.md for deploy-safety checks
• use resources/local-setup.md only when environment issues are blocking execution

This reduces context noise and improves the relevance of generated help.

Ask for tradeoffs, not just code

A strong temporal-python-testing guide prompt asks the model to justify the test layer:

• Why is this an integration test instead of unit test?
• Which activities should be mocked and which should stay real?
• What code changes require replay testing?
• What belongs in CI versus local-only checks?

Those questions produce more durable test strategy than code snippets alone.

Improve prompts with realistic constraints

Mention constraints such as:

• CI runtime limits
• no Docker in developer laptops
• production histories available or not
• need to hit coverage goals
• flaky external dependencies
• multiple workflows sharing activities

Constraints force the skill to recommend patterns you can actually adopt.

Iterate after the first output

After the first result, refine with one of these follow-ups:

• “Convert this into pytest fixtures.”
• “Reduce this to the minimum deterministic test set.”
• “Show where to use mocked activities versus real ones.”
• “Add replay testing for existing workflow histories.”
• “Rewrite for our exact workflow names and task queues.”

The first answer is often a draft strategy; the second is where the temporal-python-testing skill becomes implementation-ready.

Watch for the main adoption trap

The biggest trap is expecting one testing style to solve everything. Temporal code usually needs a mix:

• fast time-skipping tests for workflow logic
• integration tests for orchestration confidence
• replay tests for safe evolution

If you use the skill with that layered mindset, the output is much more actionable and much closer to production needs.