python-testing-patterns

wshobson/agents · updated May 15, 2026

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$npx skills add https://github.com/wshobson/agents --skill python-testing-patterns
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summary

Comprehensive testing strategies for Python using pytest, fixtures, mocking, and test-driven development.

  • Covers unit, integration, functional, and performance testing with the AAA pattern (Arrange, Act, Assert) for test structure
  • Includes 10 fundamental and advanced patterns: basic tests, fixtures with setup/teardown, parameterization, mocking, exception handling, async testing, monkeypatching, temporary files, custom fixtures, and property-based testing
  • Provides test design princip
skill.md

Python Testing Patterns

Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices.

When to Use This Skill

  • Writing unit tests for Python code
  • Setting up test suites and test infrastructure
  • Implementing test-driven development (TDD)
  • Creating integration tests for APIs and services
  • Mocking external dependencies and services
  • Testing async code and concurrent operations
  • Setting up continuous testing in CI/CD
  • Implementing property-based testing
  • Testing database operations
  • Debugging failing tests

Core Concepts

1. Test Types

  • Unit Tests: Test individual functions/classes in isolation
  • Integration Tests: Test interaction between components
  • Functional Tests: Test complete features end-to-end
  • Performance Tests: Measure speed and resource usage

2. Test Structure (AAA Pattern)

  • Arrange: Set up test data and preconditions
  • Act: Execute the code under test
  • Assert: Verify the results

3. Test Coverage

  • Measure what code is exercised by tests
  • Identify untested code paths
  • Aim for meaningful coverage, not just high percentages

4. Test Isolation

  • Tests should be independent
  • No shared state between tests
  • Each test should clean up after itself

Quick Start

# test_example.py
def add(a, b):
    return a + b

def test_add():
    """Basic test example."""
    result = add(2, 3)
    assert result == 5

def test_add_negative():
    """Test with negative numbers."""
    assert add(-1, 1) == 0

# Run with: pytest test_example.py

Fundamental Patterns

Pattern 1: Basic pytest Tests

# test_calculator.py
import pytest

class Calculator:
    """Simple calculator for testing."""

    def add(self, a: float, b: float) -> float:
        return a + b

    def subtract(self, a: float, b: float) -> float:
        return a - b

    def multiply(self, a: float, b: float) -> float:
        return a * b

    def divide(self, a: float, b: float) -> float:
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b


def test_addition():
    """Test addition."""
    calc = Calculator()
    assert calc.add(2, 3) == 5
    assert calc.add(-1, 1) == 0
    assert calc.add(0, 0) == 0


def test_subtraction():
    """Test subtraction."""
    calc = Calculator()
    assert calc.subtract(5, 3) == 2
    assert calc.subtract(0, 5) == -5


def test_multiplication():
    """Test multiplication."""
    calc = Calculator()
    assert calc.multiply(3, 4) == 12
    assert calc.multiply(0, 5) == 0


def test_division():
    """Test division."""
    calc = Calculator()
    assert calc.divide(6, 3) == 2
    assert calc.divide(5, 2) == 2.5


def test_division_by_zero():
    """Test division by zero raises error."""
    calc = Calculator()
    with pytest.raises(ValueError, match="Cannot divide by zero"):
        calc.divide(5, 0)

Pattern 2: Fixtures for Setup and Teardown

# test_database.py
import pytest
from typing import Generator

class Database:
    """Simple database class."""

    def __init__(self, connection_string: str):
        self.connection_string = connection_string
        self.connected = False

    def connect(self):
        """Connect to database."""
        self.connected = True

    def disconnect(self):
        """Disconnect from database."""
        self.connected = False

    def query(self, sql: str) -> list:
        """Execute query."""
        if not self.connected:
            raise RuntimeError("Not connected")
        return [{"id": 1, "name": "Test"}]


@pytest.fixture
def db() -> Generator[Database, None, None]:
    """Fixture that provides connected database."""
    # Setup
    database = Database("sqlite:///:memory:")
    database.connect()

    # Provide to test
    yield database

    # Teardown
    database.disconnect()


def test_database_query(db):
    """Test database query with fixture."""
    results = db.query("SELECT * FROM users")
    assert len(results) == 1
    assert results[0]["name"] == "Test"


@pytest.fixture(scope="session")
def app_config():
    """Session-scoped fixture - created once per test session."""
    return {
        "database_url": "postgresql://localhost/test",
how to use python-testing-patterns

How to use python-testing-patterns on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add python-testing-patterns
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill python-testing-patterns

The skills CLI fetches python-testing-patterns from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/python-testing-patterns

Reload or restart Cursor to activate python-testing-patterns. Access the skill through slash commands (e.g., /python-testing-patterns) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

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

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.846 reviews
  • Hassan Menon· Dec 24, 2024

    python-testing-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mei Huang· Dec 12, 2024

    python-testing-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Shikha Mishra· Dec 4, 2024

    python-testing-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 23, 2024

    python-testing-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hassan Bansal· Nov 15, 2024

    python-testing-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Lucas Farah· Nov 3, 2024

    I recommend python-testing-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Tariq Khan· Oct 22, 2024

    Useful defaults in python-testing-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Oct 14, 2024

    Keeps context tight: python-testing-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Hassan Thomas· Oct 6, 2024

    We added python-testing-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aisha Abebe· Sep 25, 2024

    Solid pick for teams standardizing on skills: python-testing-patterns is focused, and the summary matches what you get after install.

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