Python Testing logo

Python Testing

Community
manikosto
python-testing

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

Overview

Publishermanikosto
Repositoryclaude-code-python-stack
Skill namepython-testing
Stars
54
Forks
10
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by manikosto on GitHub. Read the source before you install it.

Installation

Install the Python Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/manikosto/claude-code-python-stack/tree/main/skills/python-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/manikosto/claude-code-python-stack.git /tmp/claude-code-python-stack
mkdir -p .claude/skills
cp -r /tmp/claude-code-python-stack/skills/python-testing .claude/skills/python-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Python Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Python Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Python Testing Patterns

Comprehensive testing strategies for Python applications using pytest, TDD methodology, and best practices.

When to Activate

  • Writing new Python code (follow TDD: red, green, refactor)
  • Designing test suites for Python projects
  • Reviewing Python test coverage
  • Setting up testing infrastructure

Core Testing Philosophy

Test-Driven Development (TDD)

Always follow the TDD cycle:

  1. RED: Write a failing test for the desired behavior
  2. GREEN: Write minimal code to make the test pass
  3. REFACTOR: Improve code while keeping tests green
python
# Step 1: Write failing test (RED)
def test_add_numbers():
    result = add(2, 3)
    assert result == 5

# Step 2: Write minimal implementation (GREEN)
def add(a, b):
    return a + b

# Step 3: Refactor if needed (REFACTOR)

Coverage Requirements

  • Target: 80%+ code coverage
  • Critical paths: 100% coverage required
  • Use pytest --cov to measure coverage
bash
pytest --cov=mypackage --cov-report=term-missing --cov-report=html

pytest Fundamentals

Basic Test Structure

python
import pytest

def test_addition():
    """Test basic addition."""
    assert 2 + 2 == 4

def test_string_uppercase():
    """Test string uppercasing."""
    text = "hello"
    assert text.upper() == "HELLO"

def test_list_append():
    """Test list append."""
    items = [1, 2, 3]
    items.append(4)
    assert 4 in items
    assert len(items) == 4

Assertions

python
# Equality
assert result == expected

# Inequality
assert result != unexpected

# Truthiness
assert result  # Truthy
assert not result  # Falsy
assert result is True  # Exactly True
assert result is None  # Exactly None

# Membership
assert item in collection
assert item not in collection

# Type checking
assert isinstance(result, str)

# Exception testing (preferred approach)
with pytest.raises(ValueError):
    raise ValueError("error message")

# Check exception message
with pytest.raises(ValueError, match="invalid input"):
    raise ValueError("invalid input provided")

Fixtures

Basic Fixture Usage

python
import pytest

@pytest.fixture
def sample_data():
    """Fixture providing sample data."""
    return {"name": "Alice", "age": 30}

def test_sample_data(sample_data):
    """Test using the fixture."""
    assert sample_data["name"] == "Alice"
    assert sample_data["age"] == 30

Fixture with Setup/Teardown

python
@pytest.fixture
def database():
    """Fixture with setup and teardown."""
    # Setup
    db = Database(":memory:")
    db.create_tables()
    db.insert_test_data()

    yield db  # Provide to test

    # Teardown
    db.close()

def test_database_query(database):
    """Test database operations."""
    result = database.query("SELECT * FROM users")
    assert len(result) > 0

Fixture Scopes

python
# Function scope (default) - runs for each test
@pytest.fixture
def temp_file():
    with open("temp.txt", "w") as f:
        yield f
    os.remove("temp.txt")

# Module scope - runs once per module
@pytest.fixture(scope="module")
def module_db():
    db = Database(":memory:")
    db.create_tables()
    yield db
    db.close()

# Session scope - runs once per test session
@pytest.fixture(scope="session")
def shared_resource():
    resource = ExpensiveResource()
    yield resource
    resource.cleanup()

Conftest.py for Shared Fixtures

python
# tests/conftest.py
import pytest

@pytest.fixture
def client():
    """Shared fixture for all tests."""
    app = create_app(testing=True)
    with app.test_client() as client:
        yield client

@pytest.fixture
def auth_headers(client):
    """Generate auth headers for API testing."""
    response = client.post("/api/login", json={
        "username": "test",
        "password": "test"
    })
    token = response.json["token"]
    return {"Authorization": f"Bearer {token}"}

Parametrization

Basic Parametrization

python
@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("world", "WORLD"),
    ("PyThOn", "PYTHON"),
])
def test_uppercase(input, expected):
    """Test runs 3 times with different inputs."""
    assert input.upper() == expected

Parametrize with IDs

python
@pytest.mark.parametrize("input,expected", [
    ("valid@email.com", True),
    ("invalid", False),
    ("@no-domain.com", False),
], ids=["valid-email", "missing-at", "missing-domain"])
def test_email_validation(input, expected):
    """Test email validation with readable test IDs."""
    assert is_valid_email(input) is expected

Mocking and Patching

Mocking Functions

python
from unittest.mock import patch, Mock

@patch("mypackage.external_api_call")
def test_with_mock(api_call_mock):
    """Test with mocked external API."""
    api_call_mock.return_value = {"status": "success"}

    result = my_function()

    api_call_mock.assert_called_once()
    assert result["status"] == "success"

Mocking Exceptions

python
@patch("mypackage.api_call")
def test_api_error_handling(api_call_mock):
    """Test error handling with mocked exception."""
    api_call_mock.side_effect = ConnectionError("Network error")

    with pytest.raises(ConnectionError):
        api_call()

    api_call_mock.assert_called_once()

Testing Async Code

Async Tests with pytest-asyncio

python
import pytest

@pytest.mark.asyncio
async def test_async_function():
    """Test async function."""
    result = await async_add(2, 3)
    assert result == 5

@pytest.mark.asyncio
async def test_async_with_fixture(async_client):
    """Test async with async fixture."""
    response = await async_client.get("/api/users")
    assert response.status_code == 200

Test Organization

Directory Structure

tests/
├── conftest.py                 # Shared fixtures
├── __init__.py
├── unit/                       # Unit tests
│   ├── __init__.py
│   ├── test_models.py
│   ├── test_utils.py
│   └── test_services.py
├── integration/                # Integration tests
│   ├── __init__.py
│   ├── test_api.py
│   └── test_database.py
└── e2e/                        # End-to-end tests
    ├── __init__.py
    └── test_user_flow.py

Best Practices

DO

  • Follow TDD: Write tests before code (red-green-refactor)
  • Test one thing: Each test should verify a single behavior
  • Use descriptive names: test_user_login_with_invalid_credentials_fails
  • Use fixtures: Eliminate duplication with fixtures
  • Mock external dependencies: Don't depend on external services
  • Test edge cases: Empty inputs, None values, boundary conditions
  • Aim for 80%+ coverage: Focus on critical paths
  • Keep tests fast: Use marks to separate slow tests

DON'T

  • Don't test implementation: Test behavior, not internals
  • Don't use complex conditionals in tests: Keep tests simple
  • Don't ignore test failures: All tests must pass
  • Don't test third-party code: Trust libraries to work
  • Don't share state between tests: Tests should be independent

Running Tests

bash
# Run all tests
pytest

# Run specific file
pytest tests/test_utils.py

# Run specific test
pytest tests/test_utils.py::test_function

# Run with verbose output
pytest -v

# Run with coverage
pytest --cov=mypackage --cov-report=html

# Run only fast tests
pytest -m "not slow"

# Run until first failure
pytest -x

# Run last failed tests
pytest --lf

# Run tests with pattern
pytest -k "test_user"

# Run with debugger on failure
pytest --pdb

Quick Reference

PatternUsage
pytest.raises()Test expected exceptions
@pytest.fixture()Create reusable test fixtures
@pytest.mark.parametrize()Run tests with multiple inputs
@pytest.mark.slowMark slow tests
@patch()Mock functions and classes
tmp_path fixtureAutomatic temp directory
pytest --covGenerate coverage report
assertSimple and readable assertions

Remember: Tests are code too. Keep them clean, readable, and maintainable. Good tests catch bugs; great tests prevent them.

Frequently asked questions

What does the Python Testing AI skill do?

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

Why use Python Testing on TypingMind?

Because you install it once and use it with any model. Python Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Python Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/manikosto/claude-code-python-stack/tree/main/skills/python-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Python Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Python Testing?

As many as you like. As long as a model supports skills, you can use Python Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Python Testing AI skill free?

It is published on GitHub by manikosto. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇