
Designing Tests
- 5 installs
- 250 repo stars
- Updated January 10, 2026
- cloudai-x/opencode-workflow
Helps with testing & qa tasks.
About
designing-tests is a Claude Code skill for testing & qa. It helps solo builders move faster with AI-assisted development.
- designing-tests
- Testing & QA
- AI-coding skill
Designing Tests by the numbers
- 5 all-time installs (skills.sh)
- Ranked #1,611 of 2,153 Testing & QA skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 5 |
|---|---|
| repo stars | ★ 250 |
| Last updated | January 10, 2026 |
| Repository | cloudai-x/opencode-workflow ↗ |
What it does
Helps with testing & qa tasks.
Files
Designing Tests
Strategies and patterns for designing effective, maintainable test suites.
When to Use This Skill
- Planning test coverage for new features
- Choosing between testing approaches (TDD, BDD)
- Designing integration or E2E tests
- Improving existing test suites
- Setting up testing infrastructure
- Debugging flaky tests
---
The Testing Pyramid
┌─────────┐
│ E2E │ ← Few, slow, expensive
│ Tests │ (Selenium, Playwright)
├─────────┤
│ │
┌──┤ Integr- │ ← Some, medium speed
│ │ ation │ (API tests, DB tests)
│ │ Tests │
│ ├─────────┤
│ │ │
│ │ Unit │ ← Many, fast, cheap
│ │ Tests │ (Pure functions, isolated)
└──┴─────────┘| Level | Speed | Scope | Quantity | Purpose |
|---|---|---|---|---|
| Unit | ~ms | Single function/class | Many (70-80%) | Logic correctness |
| Integration | ~s | Multiple components | Some (15-20%) | Component interaction |
| E2E | ~10s+ | Full system | Few (5-10%) | User flows work |
---
Test-Driven Development (TDD)
The Red-Green-Refactor Cycle
┌─────────────────────────────────┐
│ │
▼ │
┌─────────┐ ┌─────────┐ ┌────────┴──┐
│ RED │───▶│ GREEN │───▶│ REFACTOR │
│ Write │ │ Make │ │ Clean │
│ failing │ │ it │ │ up │
│ test │ │ pass │ │ code │
└─────────┘ └─────────┘ └───────────┘TDD Best Practices
1. Write the test first - Don't write production code without a failing test 2. Write the minimal test - One behavior per test 3. Write the minimal code - Just enough to pass 4. Refactor ruthlessly - Clean up after green 5. Run tests frequently - After every small change
TDD Example Flow
# Step 1: RED - Write failing test
def test_calculate_total_with_discount():
order = Order(items=[Item(price=100)])
order.apply_discount(10) # 10%
assert order.total() == 90
# Step 2: GREEN - Minimal implementation
class Order:
def __init__(self, items):
self.items = items
self.discount = 0
def apply_discount(self, percent):
self.discount = percent
def total(self):
subtotal = sum(i.price for i in self.items)
return subtotal * (100 - self.discount) / 100
# Step 3: REFACTOR - Clean up (if needed)---
Behavior-Driven Development (BDD)
Gherkin Syntax
Feature: Shopping Cart
As a customer
I want to add items to my cart
So that I can purchase them later
Scenario: Add item to empty cart
Given I have an empty cart
When I add a product "Widget" priced at $10
Then my cart should contain 1 item
And my cart total should be $10
Scenario: Apply discount code
Given I have a cart with total $100
When I apply discount code "SAVE10"
Then my cart total should be $90BDD Benefits
- Tests as documentation
- Shared language with stakeholders
- Focus on behavior, not implementation
- Easy to understand test intent
---
Test Design Patterns
Arrange-Act-Assert (AAA)
def test_user_registration():
# Arrange - Set up preconditions
user_data = {"email": "test@example.com", "password": "secure123"}
user_service = UserService(mock_repository)
# Act - Perform the action
result = user_service.register(user_data)
# Assert - Verify the outcome
assert result.success is True
assert result.user.email == "test@example.com"Given-When-Then (BDD style)
def test_order_cancellation():
# Given - a confirmed order
order = create_confirmed_order()
# When - the customer cancels it
order.cancel()
# Then - the order is cancelled and refund initiated
assert order.status == "cancelled"
assert order.refund_initiated is TrueTest Data Builders
class UserBuilder:
def __init__(self):
self.email = "default@test.com"
self.name = "Test User"
self.role = "user"
def with_email(self, email):
self.email = email
return self
def with_role(self, role):
self.role = role
return self
def build(self):
return User(email=self.email, name=self.name, role=self.role)
# Usage
admin = UserBuilder().with_role("admin").build()Object Mother Pattern
class TestUsers:
@staticmethod
def admin():
return User(email="admin@test.com", role="admin")
@staticmethod
def customer():
return User(email="customer@test.com", role="customer")
@staticmethod
def guest():
return User(email=None, role="guest")---
Mocking Strategies
When to Mock
| Mock | Don't Mock |
|---|---|
| External APIs | Pure business logic |
| Database (for unit tests) | Simple value objects |
| File system | Deterministic functions |
| Time/random | Core domain entities |
| Third-party services | Internal collaborators (usually) |
Mock Types
| Type | Purpose | Example |
|---|---|---|
| Stub | Return canned responses | mock.return_value = 42 |
| Mock | Verify interactions | mock.assert_called_with(...) |
| Spy | Track real calls | Wraps real object, records calls |
| Fake | Simplified implementation | In-memory database |
Mocking Example
# Using unittest.mock
from unittest.mock import Mock, patch
def test_send_email_on_registration():
# Arrange
mock_email_service = Mock()
user_service = UserService(email_service=mock_email_service)
# Act
user_service.register({"email": "test@example.com"})
# Assert
mock_email_service.send_welcome_email.assert_called_once_with("test@example.com")
# Using patch decorator
@patch("app.services.EmailService")
def test_with_patch(mock_email_class):
mock_email_class.return_value.send.return_value = True
# Test code...---
Integration Test Patterns
Database Tests
import pytest
from testcontainers.postgres import PostgresContainer
@pytest.fixture(scope="session")
def database():
with PostgresContainer("postgres:15") as postgres:
yield postgres.get_connection_url()
def test_user_persistence(database):
repo = UserRepository(database)
user = User(email="test@example.com")
repo.save(user)
retrieved = repo.find_by_email("test@example.com")
assert retrieved.email == user.emailAPI Tests
def test_create_user_endpoint(client):
response = client.post("/api/users", json={
"email": "new@example.com",
"password": "secure123"
})
assert response.status_code == 201
assert response.json["email"] == "new@example.com"
assert "id" in response.json---
E2E Test Patterns
Page Object Model
class LoginPage:
def __init__(self, page):
self.page = page
self.email_input = page.locator("#email")
self.password_input = page.locator("#password")
self.submit_button = page.locator("button[type=submit]")
def login(self, email, password):
self.email_input.fill(email)
self.password_input.fill(password)
self.submit_button.click()
return DashboardPage(self.page)
# Usage
def test_successful_login(page):
login_page = LoginPage(page)
dashboard = login_page.login("user@example.com", "password")
assert dashboard.welcome_message.is_visible()E2E Best Practices
1. Use stable selectors - data-testid, not CSS classes 2. Wait for conditions - Not arbitrary sleeps 3. Isolate test data - Each test gets fresh data 4. Test critical paths - Happy paths, key user journeys 5. Keep them fast - Parallelize, minimize scope
---
Test Coverage Strategy
What to Cover
| Priority | What | Why |
|---|---|---|
| High | Business logic | Core value |
| High | Edge cases | Where bugs hide |
| High | Error paths | Graceful failures |
| Medium | Integration points | Contract validation |
| Low | UI layout | Brittle, low value |
| Low | Third-party code | Not your responsibility |
Coverage Metrics
| Metric | Target | Notes |
|---|---|---|
| Line coverage | 70-80% | Basic minimum |
| Branch coverage | 60-70% | Catches conditionals |
| Mutation score | 50-70% | Measures test quality |
Meaningful Coverage
HIGH VALUE:
✓ Core business logic
✓ Data transformations
✓ Error handling
✓ Security-sensitive code
LOW VALUE:
✗ Getters/setters
✗ Constructor-only classes
✗ Framework boilerplate
✗ Configuration files---
Handling Flaky Tests
Common Causes
| Cause | Solution |
|---|---|
| Timing issues | Use explicit waits, not sleep |
| Shared state | Isolate test data |
| External dependencies | Mock or use containers |
| Race conditions | Add synchronization |
| Date/time | Mock time providers |
| Random data | Seed random generators |
Flaky Test Checklist
- [ ] Is the test relying on timing?
- [ ] Is there shared state between tests?
- [ ] Is there an external dependency?
- [ ] Is the order of execution assumed?
- [ ] Is there non-deterministic data?
---
Test Organization
File Structure
tests/
├── unit/ # Unit tests
│ ├── services/
│ │ └── test_user_service.py
│ └── models/
│ └── test_order.py
├── integration/ # Integration tests
│ ├── api/
│ │ └── test_user_endpoints.py
│ └── repositories/
│ └── test_user_repository.py
├── e2e/ # End-to-end tests
│ └── test_checkout_flow.py
├── fixtures/ # Shared fixtures
│ └── factories.py
└── conftest.py # Pytest configurationNaming Conventions
# Pattern: test_[what]_[condition]_[expected]
def test_calculate_total_with_discount_returns_reduced_price():
pass
def test_login_with_invalid_password_raises_auth_error():
pass
def test_order_when_cancelled_sends_refund_notification():
pass---
Anti-Patterns to Avoid
1. Testing implementation, not behavior - Tests break on refactor 2. Large test methods - Hard to debug, unclear intent 3. Excessive mocking - Tests don't reflect reality 4. Shared mutable state - Tests affect each other 5. Ignoring test failures - Broken windows effect 6. Testing private methods - Coupling to implementation 7. No assertion - Tests that can't fail 8. Copy-paste tests - Maintenance nightmare
---
Quick Reference
PYRAMID:
Unit (70%) → Integration (20%) → E2E (10%)
TDD CYCLE:
Red → Green → Refactor
PATTERNS:
AAA: Arrange-Act-Assert
Builder: Fluent test data creation
Page Object: E2E abstraction
MOCK WHEN:
External APIs, Database (unit), Time, Random
COVERAGE:
70-80% line, focus on business logic
NAMING:
test_[what]_[condition]_[expected]