Test Driven Development

What it does

This skill enables AI agents to execute Test-Driven Development (TDD) workflows, ensuring code quality by generating tests before implementation. It guides the agent through writing failing tests first, then coding the minimum solution to pass them, and finally refactoring for maintainability.

When to use it

• Preventing regressions: Before deploying critical updates to an existing codebase where breaking changes must be avoided.
• Learning new libraries: When integrating unfamiliar frameworks or APIs to ensure correct usage patterns immediately.
• Refactoring legacy code: To safely modify complex functions while maintaining a safety net of passing assertions.
• Enforcing standards: Guaranteeing that all generated code adheres to specific architectural constraints or edge-case requirements.

Key capabilities

• Generates unit tests prior to writing production logic.
• Iteratively refactors code to improve structure without breaking tests.
• Validates implementation against defined specifications and edge cases.
• Provides immediate feedback on code correctness before finalization.

Example prompts

• "Apply TDD to create a Python function that calculates compound interest, writing the test suite first."
• "Refactor this legacy JavaScript module using Test-Driven Development principles to improve readability."
• "Generate failing tests for a new REST API endpoint, then implement the logic to make them pass."

Tips & gotchas

Ensure you provide clear acceptance criteria or expected inputs/outputs when requesting test generation, as vague requirements lead to weak test cases. This workflow is most effective when the AI agent has access to the full project context to understand dependencies and existing patterns.