Mock

/mock - Test Mock & Fixture Generator

Generate mock objects, fixtures, and test data.

Usage

/mock ClassName                # Generate mock for class
/mock path/to/module.py        # Generate mocks for module
/mock --pytest                 # Pytest fixtures style
/mock --factory                # Factory Boy style

What This Skill Does

1. Analyze Code - Parse classes, functions, dependencies
2. Identify Dependencies - External services, databases, APIs
3. Generate Mocks - unittest.mock or pytest-mock style
4. Create Fixtures - Reusable test data
5. Add Factories - Factory Boy patterns for models

Output Formats

Pytest Fixtures

import pytest
from unittest.mock import Mock, MagicMock, patch

@pytest.fixture
def mock_database():
    """Mock database connection."""
    db = MagicMock()
    db.query.return_value = [{"id": 1, "name": "Test"}]
    db.insert.return_value = True
    return db

@pytest.fixture
def mock_api_client():
    """Mock external API client."""
    client = MagicMock()
    client.get.return_value = {"status": "ok", "data": []}
    client.post.return_value = {"id": 123}
    return client

@pytest.fixture
def sample_user():
    """Sample user for testing."""
    return {
        "id": 1,
        "username": "testuser",
        "email": "test@example.com",
        "created_at": "2024-01-01T00:00:00Z"
    }

Factory Boy

import factory
from factory import fuzzy
from myapp.models import User, Order

class UserFactory(factory.Factory):
    class Meta:
        model = User

    id = factory.Sequence(lambda n: n)
    username = factory.Faker('user_name')
    email = factory.Faker('email')
    created_at = factory.Faker('date_time')

class OrderFactory(factory.Factory):
    class Meta:
        model = Order

    id = factory.Sequence(lambda n: n)
    user = factory.SubFactory(UserFactory)
    total = fuzzy.FuzzyDecimal(10.0, 1000.0)
    status = fuzzy.FuzzyChoice(['pending', 'completed', 'cancelled'])

Context Managers for Patching

@pytest.fixture
def mock_external_services():
    """Patch all external service calls."""
    with patch('myapp.services.api_client') as mock_api, \
         patch('myapp.services.db_client') as mock_db, \
         patch('myapp.services.cache') as mock_cache:

        mock_api.get.return_value = {"data": []}
        mock_db.query.return_value = []
        mock_cache.get.return_value = None

        yield {
            'api': mock_api,
            'db': mock_db,
            'cache': mock_cache
        }

Mock Patterns

Return Values

mock.method.return_value = "result"
mock.method.side_effect = [1, 2, 3]  # Sequential returns
mock.method.side_effect = ValueError("error")  # Raise exception

Assertions

mock.method.assert_called_once()
mock.method.assert_called_with(arg1, arg2)
mock.method.assert_not_called()
assert mock.method.call_count == 3

Async Mocks

from unittest.mock import AsyncMock

@pytest.fixture
def mock_async_client():
    client = AsyncMock()
    client.fetch.return_value = {"data": []}
    return client

Instructions for Claude

When /mock is invoked:

1. Analyze target - Class, module, or function
2. Find dependencies - External calls, I/O, databases
3. Determine mock type - Simple mock, MagicMock, AsyncMock
4. Generate fixtures - Pytest style by default
5. Add sample data - Realistic test values
6. Include assertions - Common assertion patterns
7. Handle async - Use AsyncMock where needed
8. Write to conftest.py - If project-wide fixtures