pytest
Provides comprehensive guidance for pytest testing framework including test writing, fixtures, parametrization, mocking, and plugins. Use when the user asks about pytest, needs to write Python tests, use pytest fixtures, or configure pytest for Python projects.
Best use case
pytest is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Provides comprehensive guidance for pytest testing framework including test writing, fixtures, parametrization, mocking, and plugins. Use when the user asks about pytest, needs to write Python tests, use pytest fixtures, or configure pytest for Python projects.
Provides comprehensive guidance for pytest testing framework including test writing, fixtures, parametrization, mocking, and plugins. Use when the user asks about pytest, needs to write Python tests, use pytest fixtures, or configure pytest for Python projects.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "pytest" skill to help with this workflow task. Context: Provides comprehensive guidance for pytest testing framework including test writing, fixtures, parametrization, mocking, and plugins. Use when the user asks about pytest, needs to write Python tests, use pytest fixtures, or configure pytest for Python projects.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pytest/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pytest Compares
| Feature / Agent | pytest | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Provides comprehensive guidance for pytest testing framework including test writing, fixtures, parametrization, mocking, and plugins. Use when the user asks about pytest, needs to write Python tests, use pytest fixtures, or configure pytest for Python projects.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
## 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
```python
def test_addition():
assert 1 + 1 == 2
def test_string_upper():
assert "hello".upper() == "HELLO"
```
### 2. Fixtures
```python
import pytest
@pytest.fixture
def sample_user():
return {"name": "Alice", "email": "alice@example.com"}
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
```python
import pytest
@pytest.mark.parametrize("input,expected", [
("hello", 5),
("", 0),
("pytest", 6),
])
def test_string_length(input, expected):
assert len(input) == expected
```
### 4. Mocking
```python
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
```bash
# 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
- Official documentation: https://docs.pytest.org/
- pytest-cov: https://pytest-cov.readthedocs.io/
- pytest-xdist: https://github.com/pytest-dev/pytest-xdist
## Keywords
pytest, Python testing, fixtures, parametrize, mocking, conftest, coverage, pytest-cov, pytest-xdist, unit testing, markersRelated Skills
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