TE

testing-python

This skill assists developers in writing focused, self-contained Python unit tests, including async and parameterized configurations.

Install

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Activation

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Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
229 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Design atomic unit tests for single behaviors
  • Implement parameterization for input variations
  • Mock external services using AsyncMock
  • Use in-memory transport for FastMCP testing
  • Generate and update inline snapshots for complex data
  • Verify error handling with pytest raises

How it works

The skill enforces atomic test design and specific pytest configurations like global async mode. It utilizes in-memory transport for server testing and inline snapshots for verifying complex data structures.

Inputs & outputs

You give it
Python source code and test requirements
You get back
Validated test suite with coverage for specific behaviors

When to use testing-python

  • Writing new unit tests for Python functions
  • Refactoring legacy code into testable units
  • Debugging failing pytest cases
  • Implementing test fixtures and parameterization

About this skill

Writing Effective Python Tests

Core Principles

Every test should be atomic, self-contained, and test single functionality. A test that tests multiple things is harder to debug and maintain.

Test Structure

Atomic unit tests

Each test should verify a single behavior. The test name should tell you what's broken when it fails. Multiple assertions are fine when they all verify the same behavior.

# Good: Name tells you what's broken
def test_user_creation_sets_defaults():
    user = User(name="Alice")
    assert user.role == "member"
    assert user.id is not None
    assert user.created_at is not None

# Bad: If this fails, what behavior is broken?
def test_user():
    user = User(name="Alice")
    assert user.role == "member"
    user.promote()
    assert user.role == "admin"
    assert user.can_delete_others()

Use parameterization for variations of the same concept

import pytest

@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("World", "WORLD"),
    ("", ""),
    ("123", "123"),
])
def test_uppercase_conversion(input, expected):
    assert input.upper() == expected

Use separate tests for different functionality

Don't parameterize unrelated behaviors. If the test logic differs, write separate tests.

Project-Specific Rules

No async markers needed

This project uses asyncio_mode = "auto" globally. Write async tests without decorators:

# Correct
async def test_async_operation():
    result = await some_async_function()
    assert result == expected

# Wrong - don't add this
@pytest.mark.asyncio
async def test_async_operation():
    ...

Imports at module level

Put ALL imports at the top of the file:

# Correct
import pytest
from fastmcp import FastMCP
from fastmcp.client import Client

async def test_something():
    mcp = FastMCP("test")
    ...

# Wrong - no local imports
async def test_something():
    from fastmcp import FastMCP  # Don't do this
    ...

Use in-memory transport for testing

Pass FastMCP servers directly to clients:

from fastmcp import FastMCP
from fastmcp.client import Client

mcp = FastMCP("TestServer")

@mcp.tool
def greet(name: str) -> str:
    return f"Hello, {name}!"

async def test_greet_tool():
    async with Client(mcp) as client:
        result = await client.call_tool("greet", {"name": "World"})
        assert result[0].text == "Hello, World!"

Only use HTTP transport when explicitly testing network features.

Inline snapshots for complex data

Use inline-snapshot for testing JSON schemas and complex structures:

from inline_snapshot import snapshot

def test_schema_generation():
    schema = generate_schema(MyModel)
    assert schema == snapshot()  # Will auto-populate on first run

Commands:

  • pytest --inline-snapshot=create - populate empty snapshots
  • pytest --inline-snapshot=fix - update after intentional changes

Fixtures

Prefer function-scoped fixtures

@pytest.fixture
def client():
    return Client()

async def test_with_client(client):
    result = await client.ping()
    assert result is not None

Use tmp_path for file operations

def test_file_writing(tmp_path):
    file = tmp_path / "test.txt"
    file.write_text("content")
    assert file.read_text() == "content"

Mocking

Mock at the boundary

from unittest.mock import patch, AsyncMock

async def test_external_api_call():
    with patch("mymodule.external_client.fetch", new_callable=AsyncMock) as mock:
        mock.return_value = {"data": "test"}
        result = await my_function()
        assert result == {"data": "test"}

Don't mock what you own

Test your code with real implementations when possible. Mock external services, not internal classes.

Test Naming

Use descriptive names that explain the scenario:

# Good
def test_login_fails_with_invalid_password():
def test_user_can_update_own_profile():
def test_admin_can_delete_any_user():

# Bad
def test_login():
def test_update():
def test_delete():

Error Testing

import pytest

def test_raises_on_invalid_input():
    with pytest.raises(ValueError, match="must be positive"):
        calculate(-1)

async def test_async_raises():
    with pytest.raises(ConnectionError):
        await connect_to_invalid_host()

Running Tests

uv run pytest -n auto              # Run all tests in parallel
uv run pytest -n auto -x           # Stop on first failure
uv run pytest path/to/test.py      # Run specific file
uv run pytest -k "test_name"       # Run tests matching pattern
uv run pytest -m "not integration" # Exclude integration tests

Checklist

Before submitting tests:

  • Each test tests one thing
  • No @pytest.mark.asyncio decorators
  • Imports at module level
  • Descriptive test names
  • Using in-memory transport (not HTTP) unless testing networking
  • Parameterization for variations of same behavior
  • Separate tests for different behaviors

When not to use it

  • Testing network features requiring HTTP transport
  • Parameterizing unrelated behaviors
  • Mocking internal classes

Prerequisites

pytestinline-snapshot

Limitations

  • Requires manual exclusion of integration tests via markers
  • Restricts local imports within test functions

How it compares

Unlike manual testing, this approach mandates module-level imports and prohibits async decorators to maintain a standardized, project-specific testing structure.

Compared to similar skills

testing-python side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
testing-python (this skill)57moReviewIntermediate
python-testing-patterns772moReviewIntermediate
backtesting-frameworks172moNo flagsAdvanced
temporal-python-testing83moNo flagsAdvanced

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