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Get Started Free →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.
.claude/skills/dokhacgiakhoa-temporal-python-testing/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -59% | 0% |
Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios.
resources/implementation-playbook.md.Recommended Approach (Source: docs.temporal.io/develop/python/testing-suite):
Three Test Types:
This skill provides detailed guidance through progressive disclosure. Load specific resources based on your testing needs:
File: resources/unit-testing.md When to load: Testing individual workflows or activities in isolation Contains:
File: resources/integration-testing.md When to load: Testing workflows with mocked external dependencies Contains:
File: resources/replay-testing.md When to load: Validating determinism or deploying workflow changes Contains:
File: resources/local-setup.md When to load: Setting up development environment Contains:
pythonimport 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
pythonfrom temporalio.testing import ActivityEnvironment async def test_activity(): env = ActivityEnvironment() result = await env.run(your_activity, "test-input") assert result == expected_output
Recommended Coverage (Source: docs.temporal.io best practices):
Load specific resource when needed:
resources/unit-testing.mdresources/integration-testing.mdresources/local-setup.mdresources/replay-testing.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 9,719 | 9,918 | +2% | 1 | 1 | 0% | 920 | 2,270 | +147% | 0 | 0 | — |
case-02 | pass→pass | 9,128 | 8,558 | -6% | 1 | 1 | 0% | 743 | 1,889 | +154% | 0 | 0 | — |
case-03 | pass→pass | 16,808 | 15,291 | -9% | 1 | 1 | 0% | 2,272 | 3,300 | +45% | 0 | 0 | — |
case-04 | pass→pass | 35,171 | 8,570 | -76% | 1 | 1 | 0% | 2,071 | 1,834 | -11% | 0 | 0 | — |
case-05 | pass→pass | 13,783 | 9,959 | -28% | 1 | 1 | 0% | 2,331 | 3,090 | +33% | 0 | 0 | — |
case-06 | pass→pass | 21,828 | 11,821 | -46% | 1 | 1 | 0% | 2,859 | 3,336 | +17% | 0 | 0 | — |
case-07 | pass→pass | 11,017 | 8,828 | -20% | 1 | 1 | 0% | 1,212 | 1,899 | +57% | 0 | 0 | — |
case-08 | fail→pass | 17,551 | 9,978 | -43% | 1 | 1 | 0% | 3,493 | 3,309 | -5% | 0 | 0 | — |
case-09 | fail→pass | 21,378 | 13,747 | -36% | 1 | 1 | 0% | 3,239 | 3,003 | -7% | 0 | 0 | — |
case-10 | fail→pass | 9,960 | 9,190 | -8% | 1 | 1 | 0% | 1,662 | 1,988 | +20% | 0 | 0 | — |
case-11 | fail→pass | 14,315 | 9,362 | -35% | 1 | 1 | 0% | 1,669 | 2,065 | +24% | 0 | 0 | — |
case-12 | fail→pass | 24,785 | 7,574 | -69% | 1 | 1 | 0% | 4,007 | 1,659 | -59% | 0 | 0 | — |
case-13 | pass→pass | 17,588 | 20,562 | +17% | 1 | 1 | 0% | 2,930 | 4,085 | +39% | 0 | 0 | — |
case-14 | pass→pass | 5,561 | 2,697 | -52% | 1 | 1 | 0% | 1,048 | 1,699 | +62% | 0 | 0 | — |
case-15 | pass→pass | 11,634 | 11,652 | +0% | 1 | 1 | 0% | 2,108 | 2,416 | +15% | 0 | 0 | — |
case-16 | pass→pass | 14,662 | 18,905 | +29% | 1 | 1 | 0% | 2,284 | 3,469 | +52% | 0 | 0 | — |
case-17 | pass→pass | 12,674 | 15,811 | +25% | 1 | 1 | 0% | 2,566 | 3,043 | +19% | 0 | 0 | — |
case-18 | pass→pass | 4,180 | 7,721 | +85% | 1 | 1 | 0% | 761 | 1,685 | +121% | 0 | 0 | — |
case-19 | pass→pass | 18,371 | 16,033 | -13% | 1 | 1 | 0% | 2,748 | 3,378 | +23% | 0 | 0 | — |
case-20 | pass→pass | 14,518 | 12,721 | -12% | 1 | 1 | 0% | 1,976 | 2,637 | +33% | 0 | 0 | — |
case-21 | pass→pass | 16,831 | 17,700 | +5% | 1 | 1 | 0% | 2,511 | 3,580 | +43% | 0 | 0 | — |
case-22 | pass→pass | 20,727 | 14,059 | -32% | 1 | 1 | 0% | 3,038 | 3,024 | -0% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +23 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.