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pytest

pytest

github.com/partme-aiTesting & quality71869 installsUpdated 4w ago
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About pytest

pytest is a skill guide for Python developers writing and running tests. It covers fixtures, parametrization, mocking, configuration, coverage reports, and parallel test execution.

Key features

  1. 01Write and run Python tests
  2. 02Manage setup and teardown with fixtures
  3. 03Parametrize test inputs
  4. 04Isolate dependencies with mocks
  5. 05Configure pytest and generate coverage reports
  6. 06Run tests in parallel with pytest-xdist

Use cases

  1. 01Write unit tests for Python projects
  2. 02Reuse setup and dependency injection in tests
  3. 03Check function behavior with varied inputs
  4. 04Run tests and review coverage in CI

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/partme-ai/full-stack-skills --skill pytest

After installing, type/pytestin Claude Code to confirm it loaded.

Bundled filesLICENSE.txt

Details

From the project README

When to use this skill

Use this skill whenever the user wants to:

  • Write and run Python unit tests with pytest
  • Use fixtures for test setup, teardown, and dependency injection
  • Parametrize tests to run with multiple input sets
  • Mock dependencies with pytest-mock or unittest.mock
  • Configure pytest via pytest.ini, pyproject.toml, or conftest.py
  • Generate coverage reports with pytest-cov
  • Run tests in parallel with pytest-xdist

How to use this skill

Workflow
  1. Write test functions with test_ prefix or Test class
  2. Use fixtures in conftest.py for shared setup and dependency injection
  3. Run tests with pytest CLI, applying markers and filters as needed
  4. Analyze results with coverage and reporting plugins
1. Basic Test
def test_addition():
    assert 1 + 1 == 2

def test_string_upper():
    assert "hello".upper() == "HELLO"
2. Fixtures
import pytest

@pytest.fixture
def sample_user():
    return {"name": "Alice", "email": "[email protected]"}

def test_user_name(sample_user):
    assert sample_user["name"] == "Alice"

@pytest.fixture(autouse=True)
def reset_database(db):
    db.clear()
    yield
    db.clear()
3. Parametrize
import pytest

@pytest.mark.parametrize("input,expected", [
    ("hello", 5),
    ("", 0),
    ("pytest", 6),
])
def test_string_length(input, expected):
    assert len(input) == expected
4. Mocking
def test_api_call(mocker):
    mock_get = mocker.patch("requests.get")
    mock_get.return_value.json.return_value = {"status": "ok"}

    result = fetch_status()
    assert result == "ok"
    mock_get.assert_called_once()
5. Running Tests
# Run all tests
pytest

# Verbose with pattern filter
pytest -v -k "test_user"

# With coverage
pytest --cov=mypackage --cov-report=html

# Parallel execution
pytest -n auto

Best Practices

  • Keep tests independent with no shared mutable state; use fixtures for setup and teardown
  • Use clear, descriptive test names that explain the expected behavior
  • Avoid depending on test execution order; use conftest.py for shared fixtures
  • Run the full test suite in CI; use --cov for coverage and --maxfail=1 to fail fast
  • Use markers (@pytest.mark.slow) to categorize tests and run subsets

Resources

Keywords

pytest, Python testing, fixtures, parametrize, mocking, conftest, coverage, pytest-cov, pytest-xdist, unit testing, markers

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