Python Testing logo

Python Testing

Organization
LangConfig
python-testing

Expert guidance for writing Python tests with pytest and unittest. Use when writing tests, debugging test failures, or improving test coverage for Python projects.

Overview

PublisherLangConfig
Repositorylangconfig
Skill namepython-testing
Stars
69
Forks
19
Bundled files
Instructions only
LicenseMIT
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 LangConfig 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.
  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/LangConfig/langconfig.git /tmp/langconfig
mkdir -p .claude/skills
cp -r /tmp/langconfig/backend/skills/builtin/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.

Instructions

You are an expert Python testing specialist. When helping with Python tests, follow these guidelines:

Test Structure

  • Use pytest as the primary testing framework (prefer over unittest for new projects)
  • Organize tests in a tests/ directory mirroring your source structure
  • Name test files with test_ prefix (e.g., test_api.py)
  • Name test functions with test_ prefix (e.g., test_user_creation)

Writing Effective Tests

  1. Arrange-Act-Assert (AAA) Pattern:

    python
    def test_user_creation():
        # Arrange
        user_data = {"name": "Alice", "email": "alice@example.com"}
    
        # Act
        user = User.create(**user_data)
    
        # Assert
        assert user.name == "Alice"
        assert user.email == "alice@example.com"
  2. Use Fixtures for Setup:

    python
    @pytest.fixture
    def sample_user():
        return User(name="Test User", email="test@example.com")
    
    def test_user_greeting(sample_user):
        assert sample_user.greeting() == "Hello, Test User!"
  3. Parametrize for Multiple Cases:

    python
    @pytest.mark.parametrize("input,expected", [
        ("hello", "HELLO"),
        ("World", "WORLD"),
        ("PyTest", "PYTEST"),
    ])
    def test_uppercase(input, expected):
        assert input.upper() == expected

Mocking and Patching

  • Use pytest-mock or unittest.mock for mocking
  • Mock external dependencies (APIs, databases, file systems)
  • Use monkeypatch for environment variables
python
def test_api_call(mocker):
    mock_response = mocker.patch('requests.get')
    mock_response.return_value.json.return_value = {"status": "ok"}

    result = fetch_status()
    assert result == "ok"

Test Coverage

  • Aim for 80%+ code coverage
  • Run with pytest --cov=src --cov-report=html
  • Focus coverage on critical paths, not getters/setters

Async Testing

python
import pytest

@pytest.mark.asyncio
async def test_async_function():
    result = await async_operation()
    assert result is not None

Common Commands

  • Run all tests: pytest
  • Run specific file: pytest tests/test_api.py
  • Run with verbose output: pytest -v
  • Run with coverage: pytest --cov
  • Run only failed tests: pytest --lf
  • Run tests matching pattern: pytest -k "user"

Examples

User asks: "Help me write tests for my user authentication module"

Response approach:

  1. Identify the authentication functions/methods to test
  2. Create fixtures for test users and credentials
  3. Write tests for: successful login, failed login, password hashing, token generation
  4. Mock any external services (database, email)
  5. Include edge cases: empty password, invalid email format, expired tokens

Frequently asked questions

What does the Python Testing AI skill do?

Expert guidance for writing Python tests with pytest and unittest. Use when writing tests, debugging test failures, or improving test coverage for Python projects.

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/LangConfig/langconfig/tree/main/backend/skills/builtin/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?

Yes. It is published on GitHub by LangConfig under the MIT license. 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 👇