Pytest Skill logo

Pytest Skill

Organization
LambdaTest
pytest-skill

Generates production-grade pytest tests in Python with fixtures, parametrize, markers, mocking, and conftest patterns. Use when user mentions "pytest", "conftest", "@pytest.fixture", "@pytest.mark", "Python test". Triggers on: "pytest", "conftest", "Python test", "parametrize", "Python unit test".

Overview

PublisherLambdaTest
Repositoryagent-skills
Skill namepytest-skill
Stars
377
Forks
74
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Pytest Skill 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/LambdaTest/agent-skills/tree/main/pytest-skill
  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/LambdaTest/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/pytest-skill .claude/skills/pytest-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pytest Skill 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 Pytest Skill 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 Pytest Skill 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.

Pytest Testing Skill

Core Patterns

Basic Test

python
import pytest

def test_addition():
    assert 2 + 3 == 5

def test_exception():
    with pytest.raises(ValueError, match="invalid"):
        int("not_a_number")

class TestCalculator:
    def test_add(self):
        calc = Calculator()
        assert calc.add(2, 3) == 5

    def test_divide_by_zero(self):
        with pytest.raises(ZeroDivisionError):
            Calculator().divide(10, 0)

Fixtures

python
@pytest.fixture
def calculator():
    return Calculator()

@pytest.fixture
def db_connection():
    conn = Database.connect("test_db")
    yield conn  # teardown after yield
    conn.rollback()
    conn.close()

@pytest.fixture(scope="module")
def api_client():
    client = APIClient(base_url="http://localhost:8000")
    yield client
    client.logout()

# conftest.py - shared fixtures
@pytest.fixture(autouse=True)
def reset_state():
    State.reset()
    yield
    State.cleanup()

# Usage
def test_add(calculator):
    assert calculator.add(2, 3) == 5

Parametrize

python
@pytest.mark.parametrize("input,expected", [
    ("hello", 5), ("", 0), ("pytest", 6),
])
def test_string_length(input, expected):
    assert len(input) == expected

@pytest.mark.parametrize("a,b,expected", [
    (2, 3, 5), (-1, 1, 0), (0, 0, 0),
])
def test_add(calculator, a, b, expected):
    assert calculator.add(a, b) == expected

Markers

python
@pytest.mark.slow
def test_large_dataset(): ...

@pytest.mark.skip(reason="Not implemented")
def test_future_feature(): ...

@pytest.mark.skipif(sys.platform == "win32", reason="Unix only")
def test_unix_permissions(): ...

@pytest.mark.xfail(reason="Known bug #123")
def test_known_bug(): ...

Mocking

python
from unittest.mock import patch, MagicMock

def test_send_email(mocker):
    mock_smtp = mocker.patch("myapp.email.smtplib.SMTP")
    send_welcome_email("user@test.com")
    mock_smtp.return_value.sendmail.assert_called_once()

def test_api_call(mocker):
    mock_response = mocker.Mock()
    mock_response.status_code = 200
    mock_response.json.return_value = {"users": [{"name": "Alice"}]}
    mocker.patch("myapp.service.requests.get", return_value=mock_response)
    users = get_users()
    assert len(users) == 1

@patch("myapp.service.database")
def test_save_user(mock_db):
    mock_db.save.return_value = True
    assert save_user({"name": "Alice"}) is True
    mock_db.save.assert_called_once()

Assertions

python
assert x == y
assert x != y
assert x in collection
assert isinstance(obj, MyClass)
assert 0.1 + 0.2 == pytest.approx(0.3)

with pytest.raises(ValueError) as exc_info:
    raise ValueError("bad")
assert "bad" in str(exc_info.value)

Anti-Patterns

BadGoodWhy
self.assertEqual()assert x == ypytest rewrites give better output
Setup in __init__@pytest.fixtureLifecycle management
Global stateFixture with yieldProper cleanup
Huge test functionsSmall focused testsEasier debugging

Quick Reference

TaskCommand
Run allpytest
Run filepytest tests/test_login.py
Run specificpytest tests/test_login.py::test_login_success
By markerpytest -m slow
By keywordpytest -k "login and not invalid"
Verbosepytest -v
Stop first failpytest -x
Last failedpytest --lf
Coveragepytest --cov=myapp --cov-report=html
Parallelpytest -n auto (pytest-xdist)

pyproject.toml

toml
[tool.pytest.ini_options]
testpaths = ["tests"]
markers = ["slow: slow tests", "integration: integration tests"]
addopts = "-v --tb=short"

Deep Patterns

For production-grade patterns, see reference/playbook.md:

SectionWhat's Inside
§1 Configpytest.ini + pyproject.toml with markers, coverage
§2 FixturesScoping, factories, teardown, autouse, tmp_path
§3 ParametrizeBasic, with IDs, cartesian, indirect
§4 Mockingpytest-mock, monkeypatch, spies, env vars
§5 Asyncpytest-asyncio, async fixtures, async client
§6 Exceptionspytest.raises(match=), warnings
§7 Markers & PluginsCustom markers, collection hooks
§8 Class-BasedNested classes, autouse setup
§9 CI/CDGitHub Actions matrix, coverage gates
§10 Debugging Table10 common problems with fixes
§11 Best Practices15-item production checklist

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pytest Skill AI skill do?

Generates production-grade pytest tests in Python with fixtures, parametrize, markers, mocking, and conftest patterns. Use when user mentions "pytest", "conftest", "@pytest.fixture", "@pytest.mark", "Python test". Triggers on: "pytest", "conftest", "Python test", "parametrize", "Python unit test".

Why use Pytest Skill on TypingMind?

Because you install it once and use it with any model. Pytest Skill 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 Pytest Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LambdaTest/agent-skills/tree/main/pytest-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pytest Skill?

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 Pytest Skill?

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

Is the Pytest Skill AI skill free?

Yes. It is published on GitHub by LambdaTest 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.

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