Unit Tests

The unit-tests skill enables AI agents to generate, execute, and validate unit tests for codebases. It ensures that individual functions and classes behave as expected by creating isolated test cases that verify specific logic paths without relying on external dependencies.

When to use it

• Before deploying new features to catch regressions early in the development cycle.
• When refactoring existing code to ensure functionality remains intact after structural changes.
• To establish a safety net for critical business logic modules where failures are costly.
• During code reviews to automatically verify that proposed changes include adequate test coverage.

Key capabilities

• Generates unit tests tailored to the specific programming language and framework of the project.
• Executes test suites to report pass/fail status and identify failing assertions.
• Analyzes code structure to determine appropriate isolation boundaries for individual components.
• Provides detailed feedback on missing edge cases or uncovered code paths.

Example prompts

• "Create a comprehensive unit test suite for my Python calculate_tax function, including edge cases for zero income and negative values."
• "Run the existing unit tests for the React component UserDashboard and summarize any failures with suggested fixes."
• "Generate JUnit tests for this Java service class that handles user authentication, ensuring all password validation rules are covered."

Tips & gotchas

Ensure your codebase has proper mocking utilities available so the AI can isolate dependencies effectively during test generation. Avoid using this skill for integration or end-to-end testing scenarios, as unit tests require strict isolation from external systems like databases or APIs.