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.
npx skills add https://github.com/wshobson/agents --skill temporal-python-testing
Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios.
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:
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
from 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.mdToolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take wshobson/temporal-python-testing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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