Agent skill

testing-standards

Testing Standards Skill

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/testing-standards

SKILL.md

Testing Standards Skill

Version: 1.0.0 Category: Standards Triggers: Setting up tests, test configuration, TDD

Quick Reference

Test Framework Selection

Python: pytest

bash
uv pip install pytest pytest-cov pytest-mock pytest-asyncio

JavaScript/TypeScript: Jest Bash/Shell: bats-core

Coverage Requirements

Level Minimum Target
Line 80% 90%
Branch 75% 85%
Function 85% 95%

CI/CD fails if <80%.

Test Organization

tests/
├── unit/
│   └── test_module_name.py
├── integration/
│   └── test_module_integration.py
├── performance/
│   └── test_module_performance.py
├── fixtures/
│   └── sample_data.json
└── conftest.py

TDD Workflow

  1. RED → Write failing test
  2. GREEN → Write minimal code to pass
  3. REFACTOR → Improve while keeping tests green
  4. REPEAT → For each feature

Running Tests

Python:

bash
# Run all tests
pytest

# With coverage
pytest --cov=src --cov-report=html --cov-fail-under=80

# Specific type
pytest tests/unit/
pytest -m performance
pytest -k "test_specific_function"

# Watch mode
pytest-watch

JavaScript:

bash
npm test -- --coverage
npm test -- --watch

Test Types

Unit Tests

  • Single function/method
  • Mock external dependencies
  • Fast (<100ms per test)
  • No I/O or network

Integration Tests

  • Component interaction
  • Real dependencies where possible
  • Data flow between modules
  • Error propagation

Performance Tests

  • Time thresholds
  • Memory usage
  • Benchmarking (pytest-benchmark)
  • Run on every commit

Best Practices

Clear Assertions

python
# ✅ Good: Specific assertion with context
assert result["status"] == "success", f"Expected success, got {result['status']}"

# ❌ Bad: Generic assertion
assert result

Parametrized Tests

python
@pytest.mark.parametrize("input_data,expected", [
    ([1, 2, 3], [1, 2, 3]),
    ([None, None], []),
    ([1, None, 2], [1, 2])
])
def test_clean_data(input_data, expected):
    assert clean_data(input_data) == expected

Fixtures

python
@pytest.fixture
def database():
    db = Database(":memory:")
    db.create_tables()
    yield db
    db.close()

def test_with_fixture(database):
    result = database.query("SELECT * FROM users")

Configuration

Python (pytest.ini):

ini
[pytest]
minversion = 7.0
addopts = --cov=src --cov-fail-under=80
testpaths = tests
markers =
    performance: Performance tests
    slow: Tests taking >1 second

JavaScript (jest.config.js):

javascript
module.exports = {
  collectCoverage: true,
  coverageThresholds: {
    global: {
      branches: 75,
      functions: 85,
      lines: 80,
      statements: 80
    }
  }
};

Mock Data Guidelines

  • Use realistic data matching production scenarios
  • Include edge cases and boundary conditions
  • Version control all test fixtures
  • Document expected data formats
  • Keep fixtures minimal but representative

Performance Tests

python
def test_sort_performance(benchmark):
    data = list(range(10000, 0, -1))
    result = benchmark(sort_large_dataset, data)
    assert len(result) == 10000
    assert benchmark.stats['mean'] < 0.5  # <500ms

Compliance Checklist

  • 80%+ line coverage baseline achieved
  • Unit, integration, performance tests defined
  • Tests run in CI/CD on every commit
  • No skipped tests (@pytest.mark.skip)
  • All new features have tests
  • Edge cases tested
  • Mocking strategy documented

Full Reference

See: @docs/modules/standards/TESTING_FRAMEWORK_STANDARDS.md

Use this when setting up test infrastructure, writing tests, or debugging test failures.

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