unit-testing-test-generate
Has your agent generate comprehensive and maintainable unit tests. It ensures code works correctly by covering common cases and unexpected situations.
What it does
This tool creates automatic tests for your code. It analyzes the code you provide and generates tests that check if it works correctly, including unusual situations.
When to use it
- ✓When you have finished code and want to ensure it works.
- ✓When you want your code's tests to be done consistently.
- ✓When you need to test specific scenarios and rare cases.
Example of what to ask your AI
“Generate unit tests for this Python function that calculates the average of a list of numbers.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool reads your code, identifies parts that need testing, and creates test files. It uses techniques to ensure tests cover as much code as possible and check for errors.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill unit-testing-test-generateOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/unit-testing-test-generate/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Automated Unit Test Generation
You are a test automation expert specializing in generating comprehensive, maintainable unit tests across multiple languages and frameworks. Create tests that maximize coverage, catch edge cases, and follow best practices for assertion quality and test organization.
Use this skill when
- You need unit tests for existing code
- You want consistent test structure and coverage
- You need mocks, fixtures, and edge-case validation
Do not use this skill when
- You only need integration or E2E tests
- You cannot access the source code under test
- Tests must be hand-written for compliance reasons
Context
The user needs automated test generation that analyzes code structure, identifies test scenarios, and creates high-quality unit tests with proper mocking, assertions, and edge case coverage. Focus on framework-specific patterns and maintainable test suites.
Requirements
$ARGUMENTS
Instructions
1. Analyze Code for Test Generation
Scan codebase to identify untested code and generate comprehensive test suites:
import ast
from pathlib import Path
from typing import Dict, List, Any
class TestGenerator:
def __init__(self, language: str):
self.language = language
self.framework_map = {
'python': 'pytest',
'javascript': 'jest',
'typescript': 'jest',
'java': 'junit',
'go': 'testing'
}
def analyze_file(self, file_path: str) -> Dict[str, Any]:
"""Extract testable units from source file"""
if self.language == 'python':
return self._analyze_python(file_path)
elif self.language in ['javascript', 'typescript']:
return self._analyze_javascript(file_path)
def _analyze_python(self, file_path: str) -> Dict:
with open(file_path) as f:
tree = ast.parse(f.read())
functions = []
classes = []
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
functions.append({
'name': node.name,
'args': [arg.arg for arg in node.args.args],
'returns': ast.unparse(node.returns) if node.returns else None,
'decorators': [ast.unparse(d) for d in node.decorator_list],
'docstring': ast.get_docstring(node),
'complexity': self._calculate_complexity(node)
})
elif isinstance(node, ast.ClassDef):
methods = [n.name for n in node.body if isinstance(n, ast.FunctionDef)]
classes.append({
'name': node.name,
'methods': methods,
'bases': [ast.unparse(base) for base in node.bases]
})
return {'functions': functions, 'classes': classes, 'file': file_path}
2. Generate Python Tests with pytest
def generate_pytest_tests(self, analysis: Dict) -> str:
"""Generate pytest test file from code analysis"""
tests = ['import pytest', 'from unittest.mock import Mock, patch', '']
module_name = Path(analysis['file']).stem
tests.append(f"from {module_name} import *\n")
for func in analysis['functions']:
if func['name'].startswith('_'):
continue
test_class = self._generate_function_tests(func)
tests.append(test_class)
for cls in analysis['classes']:
test_class = self._generate_class_tests(cls)
tests.append(test_class)
return '\n'.join(tests)
def _generate_function_tests(self, func: Dict) -> str:
"""Generate test cases for a function"""
func_name = func['name']
tests = [f"\n\nclass Test{func_name.title()}:"]
# Happy path test
tests.append(f" def test_{func_name}_success(self):")
tests.append(f" result = {func_name}({self._generate_mock_args(func['args'])})")
tests.append(f" assert result is not None\n")
# Edge case tests
if len(func['args']) > 0:
tests.append(f" def test_{func_name}_with_empty_input(self):")
tests.append(f" with pytest.raises((ValueError, TypeError)):")
tests.append(f" {func_name}({self._generate_empty_args(func['args'])})\n")
# Exception handling test
tests.append(f" def test_{func_name}_handles_errors(self):")
tests.append(f" with pytest.raises(Exception):")
…
FAQ
What are unit tests?
They are small tests that check if a specific part of the code (like a function) works as expected, in isolation.
Why use this tool?
It saves time and ensures tests are comprehensive and consistent, helping to find bugs earlier.
Does it work for any programming language?
The tool supports several popular languages like Python, JavaScript, TypeScript, Java, and Go.
Details
Original description by the author
Generate comprehensive, maintainable unit tests across languages with strong coverage and edge case focus.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4443
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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