Temporal Python Testing

Temporal Python Testing Strategies

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/temporal-python-testing from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

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

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Testing Philosophy, Quick Start Guide, Coverage Targets, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• The task is unrelated to temporal python testing strategies
• You need a different domain or tool outside this scope
• 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

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Clarify goals, constraints, and required inputs.
2. Apply relevant best practices and validate outcomes.
3. Provide actionable steps and verification.
4. If detailed examples are required, open resources/implementation-playbook.md.
5. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
6. Read the overview and provenance files before loading any copied upstream support files.
7. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.

Imported Workflow Notes

Imported: Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Imported: 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)

Examples

Example 1: Ask for the upstream workflow directly

Use @temporal-python-testing to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @temporal-python-testing against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @temporal-python-testing for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @temporal-python-testing using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Time-Skipping - Month-long workflows test in seconds
• Mock Activities - Isolate workflow logic from external dependencies
• Replay Testing - Validate determinism before deployment
• High Coverage - ≥80% target for production workflows
• Fast Feedback - Unit tests run in milliseconds
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.

Imported Operating Notes

Imported: 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/temporal-python-testing, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• integration-testing.md
• local-setup.md
• replay-testing.md
• unit-testing.md

Imported Reference Notes

Imported: 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

Imported: 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

Imported: 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

Imported: Quick Start Guide

Basic Workflow Test

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

from temporalio.testing import ActivityEnvironment

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

Imported: 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

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.