unit-testing-test-generate▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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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.
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):")
tests.append(f" {func_name}({self._generate_invalid_argsHow to use unit-testing-test-generate on Cursor
AI-first code editor with Composer
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 unit-testing-test-generate
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches unit-testing-test-generate from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate unit-testing-test-generate. Access the skill through slash commands (e.g., /unit-testing-test-generate) 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
Submit your Claude Code skill and start earning
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★31 reviews- ★★★★★Kabir Harris· Dec 24, 2024
Registry listing for unit-testing-test-generate matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chaitanya Patil· Dec 12, 2024
unit-testing-test-generate reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sofia Menon· Dec 4, 2024
unit-testing-test-generate reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mateo Rao· Nov 23, 2024
I recommend unit-testing-test-generate for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Min Zhang· Nov 19, 2024
Keeps context tight: unit-testing-test-generate is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Fatima Gonzalez· Nov 15, 2024
Useful defaults in unit-testing-test-generate — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Piyush G· Nov 3, 2024
I recommend unit-testing-test-generate for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Shikha Mishra· Oct 22, 2024
Useful defaults in unit-testing-test-generate — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Lucas Gupta· Oct 14, 2024
Useful defaults in unit-testing-test-generate — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Jin Martinez· Oct 10, 2024
unit-testing-test-generate is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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