Agent skill

Python Regression Test Generator

by ArabelaTso in ArabelaTso/Skills-4-SE

Automatically generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests.

Apache-2.0Auto-check passedTesting & QA

Install Python Regression Test Generator

skills CLI
$ npx skills add ArabelaTso/Skills-4-SE --skill python-regression-test-generator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ArabelaTso/Skills-4-SE python-regression-test-generator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/python-regression-test-generator .claude/skills/python-regression-test-generator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
python-regression-test-generator
GitHub stars
253
Token cost
~4.1k tokens
SKILL.md length
673 words
Files
3 (incl. references)
Skills in repo
151
Repo updated
First seen
Licence
Apache-2.0

At a glance

Automatically generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests.

  • Works in 7 steps: Analyze Code Changes → Analyze Existing Tests → Migrate Existing Tests → …
  • Refactoring code
  • SKILL.md covers Overview, Test Generation Workflow, Complete Example and Framework-Specific Examples, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Python Regression Test Generator is an agent skill from ArabelaTso/Skills-4-SE. Automatically generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests. Migrates tests to work with new code, generates tests for new functionality, and creates mocks for external dependencies. Supports unittest and pytest frameworks. Use when refactoring code, adding features, or ensuring backward compatibility.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/api_reference.md` and `references/test_generation_patterns.md`).

It sits in Testing & QA, covering Unit testing, Refactoring and Test generation. It works with Python and pytest. The repository describes itself as: A curated list of 180+ useful Claude Skills for Software Engineering and resources for customizing AI for SE workflows. The licence is Apache-2.0.

When your agent uses it

  • Refactoring code
  • Adding features
  • Ensuring backward compatibility

Example prompts

  • “/python-regression-test-generator”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Analyze Code Changes
  2. Analyze Existing Tests
  3. Migrate Existing Tests
  4. Generate Tests for New Functionality
  5. Create Mocks for Dependencies
  6. Add Setup and Teardown
  7. Ensure Test Quality

What it can do on your machine

Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Python Regression Test Generator loads about 4.1k tokens when it runs, and up to ~9.5k if it reads all its reference files. Until then it costs about 105 tokens; SKILL.md has 673 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~105
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 673 words, ~4,105 tokens.

Download SKILL.mdSave it as .claude/skills/python-regression-test-generator/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
python-regression-test-generator
description
Automatically generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests. Migrates tests to work with new code, generates tests for new functionality, and creates mocks for external dependencies. Supports unittest and pytest frameworks. Use when refactoring code, adding features, or ensuring backward compatibility.

Python Regression Test Generator

Overview

This skill automatically generates regression tests for Python code by analyzing differences between old and new versions, migrating existing tests, and creating new tests for modified or added functionality. It ensures previously tested behavior still works while covering new code paths.

Test Generation Workflow

Follow these steps to generate regression tests:

1. Analyze Code Changes

Compare old and new versions to identify:

Function Signature Changes:

  • Added parameters (with or without defaults)
  • Removed parameters
  • Changed parameter types or names
  • Changed return types

Logic Changes:

  • Modified implementation
  • New validation rules
  • Changed error handling
  • Algorithm improvements

Structural Changes:

  • Added functions/classes/methods
  • Removed functions/classes/methods
  • Renamed functions/classes/methods
  • Moved code between modules

Example Analysis:

python
# Old version
def calculate_total(items):
    return sum(item.price for item in items)

# New version
def calculate_total(items, tax_rate=0.0):
    subtotal = sum(item.price for item in items)
    return subtotal * (1 + tax_rate)

Changes identified:

  • Added parameter: tax_rate with default value 0.0
  • Modified logic: now applies tax calculation
  • Backward compatible: old calls still work
2. Analyze Existing Tests

Review old tests to understand:

  • Test structure (unittest vs pytest)
  • Test coverage (which functions/paths are tested)
  • Test patterns (fixtures, mocks, parametrization)
  • Dependencies and setup requirements

Example Existing Test:

python
def test_calculate_total():
    items = [Item(price=10), Item(price=20)]
    assert calculate_total(items) == 30

Analysis:

  • Uses pytest style
  • Tests basic functionality
  • No mocking needed
  • Simple assertion
3. Migrate Existing Tests

Update tests to work with new code:

For backward-compatible changes:

  • Keep original test (ensures backward compatibility)
  • Add new tests for new functionality

For breaking changes:

  • Update test to match new signature/behavior
  • Document what changed in test docstring

Migrated Tests:

python
def test_calculate_total_no_tax():
    """Regression: ensure backward compatibility with no tax"""
    items = [Item(price=10), Item(price=20)]
    assert calculate_total(items) == 30  # Uses default tax_rate=0.0

def test_calculate_total_with_tax():
    """New: test tax calculation functionality"""
    items = [Item(price=10), Item(price=20)]
    assert calculate_total(items, tax_rate=0.1) == 33.0
4. Generate Tests for New Functionality

Create tests for:

  • New parameters and their effects
  • New functions/methods
  • New code paths
  • Edge cases and boundaries
  • Error conditions

Generated Tests:

python
def test_calculate_total_zero_tax():
    """Test explicit zero tax rate"""
    items = [Item(price=100)]
    assert calculate_total(items, tax_rate=0.0) == 100

def test_calculate_total_high_tax():
    """Test high tax rate"""
    items = [Item(price=100)]
    assert calculate_total(items, tax_rate=0.5) == 150

def test_calculate_total_empty_items():
    """Edge case: empty items list"""
    assert calculate_total([], tax_rate=0.1) == 0

def test_calculate_total_negative_tax():
    """Edge case: negative tax (discount)"""
    items = [Item(price=100)]
    assert calculate_total(items, tax_rate=-0.1) == 90
5. Create Mocks for Dependencies

Identify dependencies that need mocking:

  • External APIs and HTTP requests
  • Database operations
  • File I/O operations
  • Email/notification services
  • Time-dependent operations
  • Random number generation

Example with External Dependency:

New Code:

python
def send_notification(user_id, message):
    user = database.get_user(user_id)
    email_service.send_email(
        to=user.email,
        subject='Notification',
        body=message
    )
    return True

Generated Test with Mocks:

python
from unittest.mock import Mock, patch

def test_send_notification():
    """Test notification sending with mocked dependencies"""
    # Mock database
    mock_db = Mock()
    mock_user = Mock()
    mock_user.email = 'user@example.com'
    mock_db.get_user.return_value = mock_user

    # Mock email service
    with patch('myapp.email_service.send_email') as mock_send:
        mock_send.return_value = True

        with patch('myapp.database', mock_db):
            result = send_notification(user_id=123, message="Hello")

            assert result is True
            mock_db.get_user.assert_called_once_with(123)
            mock_send.assert_called_once_with(
                to='user@example.com',
                subject='Notification',
                body='Hello'
            )
6. Add Setup and Teardown

Determine if tests need:

  • Shared fixtures
  • Database setup/cleanup
  • File creation/deletion
  • State initialization

unittest Setup:

python
class TestCalculateTotal(unittest.TestCase):
    def setUp(self):
        """Run before each test"""
        self.items = [
            Item(price=10),
            Item(price=20),
            Item(price=30)
        ]

    def test_no_tax(self):
        result = calculate_total(self.items)
        self.assertEqual(result, 60)

    def test_with_tax(self):
        result = calculate_total(self.items, tax_rate=0.1)
        self.assertAlmostEqual(result, 66.0)

pytest Fixtures:

python
@pytest.fixture
def items():
    """Fixture providing test items"""
    return [
        Item(price=10),
        Item(price=20),
        Item(price=30)
    ]

def test_no_tax(items):
    result = calculate_total(items)
    assert result == 60

def test_with_tax(items):
    result = calculate_total(items, tax_rate=0.1)
    assert result == pytest.approx(66.0)
7. Ensure Test Quality

Verify generated tests are:

  • Deterministic: Same input always produces same output
  • Isolated: Tests don't depend on each other
  • Readable: Clear test names and docstrings
  • Maintainable: Follow project conventions
  • Executable: Can run without errors

Complete Example

Scenario: Adding Tax Calculation

Old Code (calculator.py):

python
class Calculator:
    def calculate_total(self, items):
        """Calculate total price of items"""
        return sum(item.price for item in items)

Old Test (test_calculator.py):

python
import unittest
from calculator import Calculator
from models import Item

class TestCalculator(unittest.TestCase):
    def test_calculate_total(self):
        calc = Calculator()
        items = [Item(price=10), Item(price=20)]
        result = calc.calculate_total(items)
        self.assertEqual(result, 30)

New Code (calculator.py):

python
class Calculator:
    def calculate_total(self, items, tax_rate=0.0, discount=0.0):
        """Calculate total price with tax and discount"""
        if tax_rate < 0 or tax_rate > 1:
            raise ValueError("Tax rate must be between 0 and 1")
        if discount < 0 or discount > 1:
            raise ValueError("Discount must be between 0 and 1")

        subtotal = sum(item.price for item in items)
        after_discount = subtotal * (1 - discount)
        total = after_discount * (1 + tax_rate)
        return round(total, 2)

Generated Regression Tests (test_calculator.py):

python
import unittest
from unittest.mock import Mock
from calculator import Calculator
from models import Item

class TestCalculator(unittest.TestCase):
    def setUp(self):
        """Set up test fixtures"""
        self.calc = Calculator()
        self.items = [Item(price=10), Item(price=20)]

    def test_calculate_total_backward_compatibility(self):
        """Regression: ensure old behavior still works"""
        result = self.calc.calculate_total(self.items)
        self.assertEqual(result, 30)

    def test_calculate_total_with_tax(self):
        """New: test tax calculation"""
        result = self.calc.calculate_total(self.items, tax_rate=0.1)
        self.assertAlmostEqual(result, 33.0)

    def test_calculate_total_with_discount(self):
        """New: test discount calculation"""
        result = self.calc.calculate_total(self.items, discount=0.2)
        self.assertAlmostEqual(result, 24.0)

    def test_calculate_total_with_tax_and_discount(self):
        """New: test combined tax and discount"""
        result = self.calc.calculate_total(
            self.items,
            tax_rate=0.1,
            discount=0.2
        )
        self.assertAlmostEqual(result, 26.4)

    def test_calculate_total_empty_items(self):
        """Edge case: empty items list"""
        result = self.calc.calculate_total([])
        self.assertEqual(result, 0)

    def test_calculate_total_invalid_tax_rate_negative(self):
        """Error case: negative tax rate"""
        with self.assertRaises(ValueError) as context:
            self.calc.calculate_total(self.items, tax_rate=-0.1)
        self.assertIn("Tax rate must be between 0 and 1", str(context.exception))

    def test_calculate_total_invalid_tax_rate_too_high(self):
        """Error case: tax rate > 1"""
        with self.assertRaises(ValueError):
            self.calc.calculate_total(self.items, tax_rate=1.5)

    def test_calculate_total_invalid_discount_negative(self):
        """Error case: negative discount"""
        with self.assertRaises(ValueError):
            self.calc.calculate_total(self.items, discount=-0.1)

    def test_calculate_total_invalid_discount_too_high(self):
        """Error case: discount > 1"""
        with self.assertRaises(ValueError):
            self.calc.calculate_total(self.items, discount=1.5)

if __name__ == '__main__':
    unittest.main()

Framework-Specific Examples

pytest Example

Old Code:

python
def fetch_user_data(user_id):
    response = requests.get(f'https://api.example.com/users/{user_id}')
    return response.json()

Old Test:

python
def test_fetch_user_data():
    with patch('requests.get') as mock_get:
        mock_get.return_value.json.return_value = {'id': 1, 'name': 'Alice'}
        result = fetch_user_data(1)
        assert result['name'] == 'Alice'

New Code:

python
async def fetch_user_data(user_id, include_posts=False):
    async with aiohttp.ClientSession() as session:
        url = f'https://api.example.com/users/{user_id}'
        if include_posts:
            url += '?include=posts'
        async with session.get(url) as response:
            return await response.json()

Generated Regression Tests:

python
import pytest
from unittest.mock import AsyncMock, patch

@pytest.mark.asyncio
async def test_fetch_user_data_backward_compatibility():
    """Regression: basic user fetch still works"""
    with patch('aiohttp.ClientSession') as mock_session:
        mock_response = AsyncMock()
        mock_response.json.return_value = {'id': 1, 'name': 'Alice'}
        mock_session.return_value.__aenter__.return_value.get.return_value.__aenter__.return_value = mock_response

        result = await fetch_user_data(1)
        assert result['name'] == 'Alice'

@pytest.mark.asyncio
async def test_fetch_user_data_with_posts():
    """New: test include_posts parameter"""
    with patch('aiohttp.ClientSession') as mock_session:
        mock_response = AsyncMock()
        mock_response.json.return_value = {
            'id': 1,
            'name': 'Alice',
            'posts': [{'id': 1, 'title': 'Post 1'}]
        }
        mock_session.return_value.__aenter__.return_value.get.return_value.__aenter__.return_value = mock_response

        result = await fetch_user_data(1, include_posts=True)
        assert 'posts' in result
        assert len(result['posts']) == 1

@pytest.mark.asyncio
@pytest.mark.parametrize("user_id,include_posts", [
    (1, False),
    (1, True),
    (999, False),
])
async def test_fetch_user_data_parametrized(user_id, include_posts):
    """Parametrized test for various inputs"""
    with patch('aiohttp.ClientSession') as mock_session:
        mock_response = AsyncMock()
        mock_response.json.return_value = {'id': user_id, 'name': 'User'}
        mock_session.return_value.__aenter__.return_value.get.return_value.__aenter__.return_value = mock_response

        result = await fetch_user_data(user_id, include_posts=include_posts)
        assert result['id'] == user_id

Test Generation Guidelines

Show full SKILL.md (296 more words)Show less
When to Migrate vs Create New

Migrate existing test when:

  • Function signature changed but is backward compatible
  • Return type changed (update assertions)
  • Exception type changed
  • Implementation changed but interface is same

Create new test when:

  • New parameters added
  • New functionality added
  • New code paths introduced
  • New edge cases discovered

Mark as obsolete when:

  • Function removed from codebase
  • Functionality completely replaced
  • Test no longer relevant
Naming Conventions

Regression tests:

  • test_<function>_backward_compatibility
  • test_<function>_<old_behavior>
  • Include "Regression:" in docstring

New functionality tests:

  • test_<function>_<new_feature>
  • test_<function>_with_<parameter>
  • Include "New:" in docstring

Edge case tests:

  • test_<function>_empty_input
  • test_<function>_boundary_<condition>
  • Include "Edge case:" in docstring

Error tests:

  • test_<function>_invalid_<parameter>
  • test_<function>_raises_<exception>
  • Include "Error case:" in docstring
Mock Generation Rules

Always mock:

  • External API calls (requests, aiohttp)
  • Database operations
  • File system operations
  • Email/SMS services
  • Time-dependent functions (datetime.now, time.sleep)

Consider mocking:

  • Complex object creation
  • Expensive computations
  • Non-deterministic operations

Don't mock:

  • Simple data structures (lists, dicts)
  • Pure functions without side effects
  • The function under test
  • Standard library functions (unless they have side effects)

Constraints

MUST:

  • Analyze both old and new code versions
  • Preserve existing valid tests
  • Generate tests for new functionality
  • Create appropriate mocks for dependencies
  • Follow existing test framework (unittest or pytest)
  • Ensure tests are deterministic and isolated
  • Include docstrings explaining test purpose
  • Use proper setup/teardown or fixtures

MUST NOT:

  • Remove valid tests without justification
  • Generate redundant tests
  • Create tests that depend on external state
  • Mock the function being tested
  • Generate non-executable test code
  • Ignore breaking changes
  • Create flaky or non-deterministic tests

Output Format

Complete test module structure:

python
"""
Regression tests for <module_name>

Generated tests ensure backward compatibility and cover new functionality
added in the latest version.
"""

import unittest  # or pytest
from unittest.mock import Mock, patch, MagicMock
# Other imports

class Test<ClassName>(unittest.TestCase):  # or pytest functions
    def setUp(self):
        """Set up test fixtures"""
        # Setup code

    def test_<name>_backward_compatibility(self):
        """Regression: <description of old behavior>"""
        # Test code

    def test_<name>_<new_feature>(self):
        """New: <description of new functionality>"""
        # Test code

    # More tests...

if __name__ == '__main__':
    unittest.main()

Resources

references/test_generation_patterns.md

Comprehensive patterns and techniques including:

  • Change analysis patterns (signature changes, logic changes, structural changes)
  • Test generation strategies (migration, behavior preservation, edge cases)
  • Testing framework support (unittest and pytest)
  • Mocking and stubbing patterns
  • Test structure patterns (AAA, Given-When-Then, setup/teardown)
  • Coverage strategies (path coverage, boundary testing, state-based testing)
  • Test migration patterns with complete examples

© ArabelaTso, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/python-regression-test-generator of ArabelaTso/Skills-4-SE.

  • SKILL.md
  • references/api_reference.md
  • references/test_generation_patterns.md

Open the folder on GitHubat commit 4f38503

Compare with similar skills

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Works with

Categories

Questions about Python Regression Test Generator

What does Python Regression Test Generator do?

Automatically generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests. Python Regression Test Generator is an agent skill from ArabelaTso/Skills-4-SE. Automatically generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests.

When should I use Python Regression Test Generator?

Python Regression Test Generator fits situations like: refactoring code; adding features; ensuring backward compatibility.

How do I install Python Regression Test Generator in Claude Code?

Run `npx skills add ArabelaTso/Skills-4-SE --skill python-regression-test-generator -a claude-code`. Or copy the skill folder (skills/python-regression-test-generator in ArabelaTso/Skills-4-SE) into .claude/skills/python-regression-test-generator in your project. Claude Code loads it when a task matches its description.

How do I install Python Regression Test Generator in Codex?

Run `npx skills add ArabelaTso/Skills-4-SE --skill python-regression-test-generator -a codex`. Or copy the skill folder (skills/python-regression-test-generator in ArabelaTso/Skills-4-SE) into .agents/skills/python-regression-test-generator in your project. Codex loads it when a task matches its description.

Can I use Python Regression Test Generator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArabelaTso/Skills-4-SE --skill python-regression-test-generator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-regression-test-generator, .gemini/skills/python-regression-test-generator, .github/skills/python-regression-test-generator and .opencode/skills/python-regression-test-generator in your project.

What does Python Regression Test Generator need to run?

SKILL.md names no scripts, command-line tools or credentials: Python Regression Test Generator is instructions for the agent only. Our summary lists: Python 3.

Does Python Regression Test Generator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Python Regression Test Generator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Python Regression Test Generator use?

Python Regression Test Generator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Python Regression Test Generator use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.4k tokens, read only when the agent opens those files.

What are the alternatives to Python Regression Test Generator?

Skills that share tags, products or a category with Python Regression Test Generator: Adk Verify Snippets (google/adk-python, 22k stars), Hermetic Python Unit Tests (dimensionalOS/dimos, 4.6k stars), Test Coverage Review (areed1192/finance-news-aggregator, 149 stars) and Py Package Check (ipea/geobr, 959 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Python Regression Test Generator?

ArabelaTso (a GitHub user) maintains it in ArabelaTso/Skills-4-SE, which has 253 GitHub stars. The repository holds 151 skills in this directory. The repository was last updated on August 21, 2026.

Source: ArabelaTso/Skills-4-SE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.