Applies proven testing patterns — Arrange-Act-Assert (AAA), Given-When-Then, Test Data Builders, Object Mother, parameterized tests, fixtures, spies, and test doubles — to help write maintainable, reliable, and readable test suites. Use when the user asks about writing unit tests, integration tests, or end-to-end tests; structuring test cases or test suites; applying TDD or BDD practices; working with mocks, stubs, spies, or fakes; improving test coverage or reducing flakiness; or needs guidance on test organization, naming conventions, or assertions in frameworks like Jest, Vitest, pytest, or similar.
npx skills add https://github.com/rohitg00/skillkit --skill test-patterns
You are applying proven testing patterns to write maintainable, reliable tests. These patterns help ensure tests are readable, focused, and trustworthy.
Use this to choose the right pattern for your situation:
Patterns rarely stand alone — here's how to combine them for common scenarios:
Unit tests (isolated logic):
Fixtures for setup → AAA structure → Stubs/Mocks for dependencies → Parameterized Tests for multiple input cases
Integration tests (service + external dependencies):
Fixtures for setup → AAA structure → Fakes for external services (e.g. in-memory DB) → Spies to verify interaction points
BDD / feature specs:
Given-When-Then → Object Mother or Test Data Builders for scenario data → Fakes for infrastructure
High-variation logic (validators, calculators, formatters):
Parameterized Tests → Test Data Builders to construct each case → AAA structure within each case
Structure every test with three distinct phases:
// Arrange - Set up test data and dependencies
const user = createTestUser({ role: 'admin' });
const service = new UserService(mockRepository);
// Act - Execute the code under test
const result = await service.updateRole(user.id, 'member');
// Assert - Verify the expected outcome
expect(result.role).toBe('member');
expect(mockRepository.save).toHaveBeenCalledWith(user);
Guidelines:
For behavior-focused tests:
describe('Shopping Cart', () => {
describe('when adding an item', () => {
it('should increase the item count', () => {
// Given
const cart = new Cart();
// When
cart.add({ id: '1', quantity: 2 });
// Then
expect(cart.itemCount).toBe(2);
});
});
});
Create flexible test data without repetition:
// Builder function
function createTestOrder(overrides = {}) {
return {
id: 'order-123',
status: 'pending',
items: [],
total: 0,
...overrides
};
}
// Usage
const completedOrder = createTestOrder({ status: 'completed', total: 99.99 });
const emptyOrder = createTestOrder({ items: [] });
Factory for complex test objects:
class TestUserFactory {
static admin() {
return new User({ role: 'admin', permissions: ALL_PERMISSIONS });
}
static guest() {
return new User({ role: 'guest', permissions: [] });
}
static withSubscription(tier) {
return new User({ subscription: { tier, active: true } });
}
}
Test multiple cases efficiently:
describe('isValidEmail', () => {
const validCases = [
'[email protected]',
'[email protected]',
'[email protected]'
];
const invalidCases = [
'',
'not-an-email',
'@no-local.com',
'no-domain@'
];
test.each(validCases)('should accept valid email: %s', (email) => {
expect(isValidEmail(email)).toBe(true);
});
test.each(invalidCases)('should reject invalid email: %s', (email) => {
expect(isValidEmail(email)).toBe(false);
});
});
Reusable test setup:
describe('OrderService', () => {
let service;
let mockPaymentGateway;
let mockInventory;
beforeEach(() => {
mockPaymentGateway = createMockPaymentGateway();
mockInventory = createMockInventory();
service = new OrderService(mockPaymentGateway, mockInventory);
});
afterEach(() => {
jest.clearAllMocks();
});
});
Verify interactions without implementation:
it('should send notification on order completion', async () => {
const notifySpy = jest.spyOn(notificationService, 'send');
await orderService.complete(orderId);
expect(notifySpy).toHaveBeenCalledWith({
type: 'order_completed',
orderId: orderId
});
});
Choose the right type:
| Type | When to Use |
|------|-------------|
| Stub | Need predictable, canned return values |
| Mock | Need to assert a dependency was called correctly |
| Spy | Partial mocking — observe calls on a real object |
| Fake | Need a working lightweight substitute (e.g. in-memory DB) |
Ensure tests don't affect each other:
Test names should describe:
Good examples:
shouldReturnEmptyArrayWhenNoItemsExistthrowsErrorWhenUserNotAuthenticatedcalculatesDiscountForPremiumMemberssrc/
services/
UserService.ts
UserService.test.ts # Co-located tests
tests/
integration/
api.test.ts # Integration tests
e2e/
checkout.spec.ts # End-to-end tests
For each test:
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.
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