Comprehensive pytest testing strategies with TDD, fixtures, mocking, and coverage best practices.
Works with
Covers TDD methodology (red-green-refactor cycle), parametrization, fixtures with multiple scopes, and mocking patterns for unit and integration testing
Includes pytest fundamentals: assertions, markers for test selection, exception testing, and async test support with pytest-asyncio
Provides practical patterns for testing APIs, databases, file operations, and class methods with real cod
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-testingExecute the skills CLI command in your project's root directory to begin installation:
Fetches python-testing from affaan-m/everything-claude-code and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate python-testing. Access via /python-testing in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Improve work quality by 30-40% with less effort
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Comprehensive testing strategies for Python applications using pytest, TDD methodology, and best practices.
Always follow the TDD cycle:
# Step 1: Write failing test (RED)
def test_add_numbers():
result = add(2, 3)
assert result == 5
# Step 2: Write minimal implementation (GREEN)
def add(a, b):
return a + b
# Step 3: Refactor if needed (REFACTOR)
pytest --cov to measure coveragepytest --cov=mypackage --cov-report=term-missing --cov-report=html
import pytest
def test_addition():
"""Test basic addition."""
assert 2 + 2 == 4
def test_string_uppercase():
"""Test string uppercasing."""
text = "hello"
assert text.upper() == "HELLO"
def test_list_append():
"""Test list append."""
items = [1, 2, 3]
items.append(4)
assert 4 in items
assert len(items) == 4
# Equality
assert result == expected
# Inequality
assert result != unexpected
# Truthiness
assert result # Truthy
assert not result # Falsy
assert result is True # Exactly True
assert result is False # Exactly False
assert result is None # Exactly None
# Membership
assert item in collection
assert item not in collection
# Comparisons
assert result > 0
assert 0 <= result <= 100
# Type checking
assert isinstance(result, str)
# Exception testing (preferred approach)
with pytest.raises(ValueError):
raise ValueError("error message")
# Check exception message
with pytest.raises(ValueError, match="invalid input"):
raise ValueError("invalid input provided")
# Check exception attributes
with pytest.raises(ValueError) as exc_info:
raise ValueError("error message")
assert str(exc_info.value) == "error message"
import pytest
@pytest.fixture
def sample_data():
"""Fixture providing sample data."""
return {"name": "Alice", "age": 30}
def test_sample_data(sample_data):
"""Test using the fixture."""
assert sample_data["name"] == "Alice"
assert sample_data["age"] == 30
@pytest.fixture
def database():
"""Fixture with setup and teardown."""
# Setup
db = Database(":memory:")
db.create_tables()
db.insert_test_data()
yield db # Provide to test
# Teardown
db.close()
def test_database_query(database):
"""Test database operations."""
result = database.query("SELECT * FROM users")
assert len(result) > 0
# Function scope (default) - runs for each test
@pytest.fixture
def temp_file():
with open("temp.txt", "w") as f:
yield f
os.remove("temp.txt")
# Module scope - runs once per module
@pytest.fixture(scope="module")
def module_db():
db = Database(":memory:")
db.create_tables()
yield db
db.close()
# Session scope - runs once per test session
@pytest.fixture(scope="session")
def shared_resource():
resource = ExpensiveResource()
yield resource
resource.cleanup()
@pytest.fixture(params=[1, 2, 3])
def number(request):
"""Parameterized fixture."""
return request.param
def test_numbers(number):
"""Test runs 3 times, once for each parameter."""
assert number > 0
@pytest.fixture
def user():
return User(id=1, name="Alice")
@pytest.fixture
def admin():
return User(id=2, name="Admin", role="admin")
def test_user_admin_interaction(user, admin):
"""Test using multiple fixtures."""
assert admin.can_manage(user)
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Backendtag: pythonpython-expert-best-practices-code-review
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Backendtag: pythonReviews
4.4★★★★★55 reviews- AAlexander Okafor★★★★★Dec 20, 2024
I recommend python-testing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- SSakura Desai★★★★★Dec 20, 2024
Solid pick for teams standardizing on skills: python-testing is focused, and the summary matches what you get after install.
- IIsabella Sethi★★★★★Dec 12, 2024
python-testing has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SShikha Mishra★★★★★Dec 8, 2024
python-testing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- RRahul Santra★★★★★Nov 27, 2024
Registry listing for python-testing matched our evaluation — installs cleanly and behaves as described in the markdown.
- YYusuf Ramirez★★★★★Nov 11, 2024
python-testing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- OOlivia Iyer★★★★★Nov 3, 2024
Keeps context tight: python-testing is the kind of skill you can hand to a new teammate without a long onboarding doc.
- LLiam Rao★★★★★Oct 22, 2024
python-testing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- PPratham Ware★★★★★Oct 18, 2024
python-testing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- FFatima Kim★★★★★Oct 2, 2024
Keeps context tight: python-testing is the kind of skill you can hand to a new teammate without a long onboarding doc.
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