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pytest skill

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This skill helps you write and refactor pytest tests effectively by guiding fixtures, mocking, parametrization, and markers for reliable Python testing.

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---
name: pytest
description: >
  Pytest testing patterns for Python.
  Trigger: When writing or refactoring pytest tests (fixtures, mocking, parametrize, markers). For Prowler-specific API/SDK testing conventions, also use prowler-test-api or prowler-test-sdk.
license: Apache-2.0
metadata:
  author: prowler-cloud
  version: "1.0"
  scope: [root, sdk, api]
  auto_invoke: "Writing Python tests with pytest"
allowed-tools: Read, Edit, Write, Glob, Grep, Bash, WebFetch, WebSearch, Task
---

## Basic Test Structure

```python
import pytest

class TestUserService:
    def test_create_user_success(self):
        user = create_user(name="John", email="[email protected]")
        assert user.name == "John"
        assert user.email == "[email protected]"

    def test_create_user_invalid_email_fails(self):
        with pytest.raises(ValueError, match="Invalid email"):
            create_user(name="John", email="invalid")
```

## Fixtures

```python
import pytest

@pytest.fixture
def user():
    """Create a test user."""
    return User(name="Test User", email="[email protected]")

@pytest.fixture
def authenticated_client(client, user):
    """Client with authenticated user."""
    client.force_login(user)
    return client

# Fixture with teardown
@pytest.fixture
def temp_file():
    path = Path("/tmp/test_file.txt")
    path.write_text("test content")
    yield path  # Test runs here
    path.unlink()  # Cleanup after test

# Fixture scopes
@pytest.fixture(scope="module")  # Once per module
@pytest.fixture(scope="class")   # Once per class
@pytest.fixture(scope="session") # Once per test session
```

## conftest.py

```python
# tests/conftest.py - Shared fixtures
import pytest

@pytest.fixture
def db_session():
    session = create_session()
    yield session
    session.rollback()

@pytest.fixture
def api_client():
    return TestClient(app)
```

## Mocking

```python
from unittest.mock import patch, MagicMock

class TestPaymentService:
    def test_process_payment_success(self):
        with patch("services.payment.stripe_client") as mock_stripe:
            mock_stripe.charge.return_value = {"id": "ch_123", "status": "succeeded"}

            result = process_payment(amount=100)

            assert result["status"] == "succeeded"
            mock_stripe.charge.assert_called_once_with(amount=100)

    def test_process_payment_failure(self):
        with patch("services.payment.stripe_client") as mock_stripe:
            mock_stripe.charge.side_effect = PaymentError("Card declined")

            with pytest.raises(PaymentError):
                process_payment(amount=100)

# MagicMock for complex objects
def test_with_mock_object():
    mock_user = MagicMock()
    mock_user.id = "user-123"
    mock_user.name = "Test User"
    mock_user.is_active = True

    result = get_user_info(mock_user)
    assert result["name"] == "Test User"
```

## Parametrize

```python
@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("world", "WORLD"),
    ("pytest", "PYTEST"),
])
def test_uppercase(input, expected):
    assert input.upper() == expected

@pytest.mark.parametrize("email,is_valid", [
    ("[email protected]", True),
    ("invalid-email", False),
    ("", False),
    ("[email protected]", False),
])
def test_email_validation(email, is_valid):
    assert validate_email(email) == is_valid
```

## Markers

```python
# pytest.ini or pyproject.toml
[tool.pytest.ini_options]
markers = [
    "slow: marks tests as slow",
    "integration: marks integration tests",
]

# Usage
@pytest.mark.slow
def test_large_data_processing():
    ...

@pytest.mark.integration
def test_database_connection():
    ...

@pytest.mark.skip(reason="Not implemented yet")
def test_future_feature():
    ...

@pytest.mark.skipif(sys.platform == "win32", reason="Unix only")
def test_unix_specific():
    ...

# Run specific markers
# pytest -m "not slow"
# pytest -m "integration"
```

## Async Tests

```python
import pytest

@pytest.mark.asyncio
async def test_async_function():
    result = await async_fetch_data()
    assert result is not None
```

## Commands

```bash
pytest                          # Run all tests
pytest -v                       # Verbose output
pytest -x                       # Stop on first failure
pytest -k "test_user"           # Filter by name
pytest -m "not slow"            # Filter by marker
pytest --cov=src                # With coverage
pytest -n auto                  # Parallel (pytest-xdist)
pytest --tb=short               # Short traceback
```

## References

For general pytest documentation, see:
- **Official Docs**: https://docs.pytest.org/en/stable/

For Prowler SDK testing with provider-specific patterns (moto, MagicMock), see:
- **Documentation**: [references/prowler-testing.md](references/prowler-testing.md)

Overview

This skill provides practical pytest testing patterns for Python projects, with examples for fixtures, mocking, parametrization, markers, async tests, and common CLI commands. It includes Prowler-specific guidance for API/SDK testing using prowler-test-api or prowler-test-sdk conventions. The content helps developers write reliable, maintainable tests and integrate tests into CI pipelines.

How this skill works

It describes standard test structure, fixture usage (including scopes and teardown via yield), and shared fixtures in conftest.py. It covers mocking with unittest.mock (patch, MagicMock), parametrized tests with @pytest.mark.parametrize, markers for categorizing tests, async test support, and common pytest CLI commands and flags. It also points to provider-specific testing patterns for Prowler SDK and API testing.

When to use it

  • Writing or refactoring unit tests for Python functions and services.
  • Creating shared setup/teardown across tests using fixtures in conftest.py.
  • Replacing real external calls with mocks for API, SDK, or payment clients.
  • Running parameterized test matrices to cover multiple inputs efficiently.
  • Tagging slow or integration tests to control CI execution and filtering.

Best practices

  • Keep fixtures focused and reuse them via conftest.py to reduce duplication.
  • Prefer yield fixtures for setup and teardown to ensure cleanup regardless of failures.
  • Mock external dependencies at the import path used by the code under test.
  • Use parametrization to reduce repetitive test functions and improve coverage.
  • Mark long-running or environment-dependent tests (integration, slow) so CI can skip or run them selectively.

Example use cases

  • Unit testing a user service: assert behavior and raised exceptions for invalid input.
  • Mocking a payment provider to simulate success and failure scenarios without network calls.
  • Testing email validation with @pytest.mark.parametrize to verify multiple cases.
  • Creating an authenticated test client with a fixture for web app endpoints.
  • Writing async tests for coroutines with @pytest.mark.asyncio.

FAQ

How do I ensure fixtures are shared across test modules?

Define them in tests/conftest.py; pytest will discover and inject them into tests in the same test session.

Where should I patch external clients to avoid breaking imports?

Patch the client at the import location used by the code under test (e.g., patch('services.payment.stripe_client')).