Unit testing and integration testing best practices
npx skills add https://github.com/langchain-ai/skills-benchmarks --skill testing-patterns
Write effective, maintainable tests using modern patterns.
def test_user_registration():
# Arrange
user_data = {"email": "[email protected]", "password": "secure123"}
# Act
result = register_user(user_data)
# Assert
assert result.success is True
assert result.user.email == "[email protected]"
from unittest.mock import Mock, patch
@patch('services.email.send_email')
def test_sends_welcome_email(mock_send):
mock_send.return_value = True
register_user({"email": "[email protected]"})
mock_send.assert_called_once_with(
to="[email protected]",
template="welcome"
)
import pytest
from factories import UserFactory
@pytest.fixture
def user():
return UserFactory.create(role="admin")
@pytest.fixture
def authenticated_client(user):
client = TestClient(app)
client.login(user)
return client
def test_admin_dashboard(authenticated_client):
response = authenticated_client.get("/admin")
assert response.status_code == 200
@pytest.mark.integration
def test_full_checkout_flow(db_session, stripe_mock):
# Create test data
user = create_user()
product = create_product(price=100)
# Execute flow
cart = add_to_cart(user, product)
order = checkout(cart, payment_method="card")
# Verify
assert order.status == "completed"
assert stripe_mock.charges.create.called
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take langchain-ai/testing-patterns from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.