test-driven-development

Enforce red-green-refactor by writing failing tests before production code.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/chriscarterux/chris-claude-stack --skill test-driven-development
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: test-driven-development
Source: https://github.com/chriscarterux/chris-claude-stack/tree/main/skills/test-driven-development
Command: npx skills add https://github.com/chriscarterux/chris-claude-stack --skill test-driven-development

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures that every piece of code you write is thoroughly tested and verified, preventing bugs and regressions. By enforcing a "test-first" approach, it eliminates the uncertainty of whether your tests actually validate behavior, leading to more robust, reliable, and easily maintainable software.

Core Features & Use Cases

  • Red-Green-Refactor Cycle: Systematically write a failing test, implement minimal code to pass it, then refactor, ensuring continuous quality and clean code.
  • Behavior-Driven Testing: Focus on testing the desired behavior of your code, rather than its internal implementation, leading to more resilient and meaningful tests.
  • Bug Prevention & Regression Safety: Catch bugs before they're committed and ensure future changes don't break existing functionality, saving significant debugging time.
  • Use Case: When implementing a new retryOperation function, first write a test that expects it to retry 3 times and eventually succeed. Watch this test fail, then write only the code necessary to make it pass, ensuring your function behaves exactly as intended.

Quick Start

RED: Write a failing test

test('retries failed operations 3 times', async () => { let attempts = 0; const operation = () => { attempts++; if (attempts < 3) throw new Error('fail'); return 'success'; }; const result = await retryOperation(operation); expect(result).toBe('success'); expect(attempts).toBe(3); });

Verify it fails, then write minimal code to pass.

Frequently Asked Questions about test-driven-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write tests before implementing code?

Test-driven development requires writing a failing test first, then implementing minimal code to pass it. Start by defining what behavior you expect, verify the test fails, add only the code necessary to make it pass, then refactor. This red-green-refactor cycle ensures your implementation matches actual requirements and remains testable.

Why should I use test-driven development for bug prevention?

Test-driven development catches bugs before code reaches production by requiring failing tests before any implementation. Each test validates specific behavior, preventing regressions when refactoring. This approach eliminates uncertainty about whether tests actually verify behavior, resulting in more reliable and maintainable software.

Can I apply test-driven development to existing features and refactoring?

Yes, test-driven development applies across new features, bug fixes, refactoring, and behavioral changes. Write a failing test that captures the desired behavior, implement minimal changes to pass it, then refactor safely. This workflow maintains code quality and confidence during refactoring by preventing unintended behavior changes.

What's the difference between testing implementation details versus behavior?

Behavior-driven testing focuses on what your code does—the observable outputs—rather than how it does it internally. This approach creates resilient tests that remain valid even after refactoring, and more meaningful tests that verify actual user-facing functionality rather than brittle implementation specifics.

How does the red-green-refactor cycle improve code quality?

The red-green-refactor cycle systematically ensures clean, tested code: red (failing test defines requirements), green (minimal code passes the test), refactor (improve without changing behavior). This discipline prevents over-engineering, keeps code focused, and maintains continuous quality by validating every change against tests.