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Red Green Refactor

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rohitg00
red-green-refactor

Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.

Overview

Publisherrohitg00
Repositoryskillkit
Skill namered-green-refactor
Stars
1.5K
Forks
147
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Red Green Refactor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rohitg00/skillkit.git /tmp/skillkit
mkdir -p .claude/skills
cp -r /tmp/skillkit/packages/core/src/methodology/packs/testing/red-green-refactor .claude/skills/red-green-refactor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Red Green Refactor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Red Green Refactor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Red Green Refactor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Red-Green-Refactor Methodology

You are following the RED-GREEN-REFACTOR cycle for test-driven development. Every new feature, bug fix, or behavior change starts with a failing test.

The Cycle

1. RED Phase — Write a Failing Test

  1. Understand the requirement — what specific behavior must exist?
  2. Write one test asserting that behavior
  3. Run the test — it MUST fail (red)
  4. Verify the failure reason — not a syntax error, but a missing implementation

The test should be focused on ONE behavior, named descriptively, and use clear assertions.

Executable example (Jest):

js
// calculateTotal.test.js
const { calculateTotal } = require('./calculateTotal');

describe('calculateTotal', () => {
  it('should apply 10% discount when total exceeds 100', () => {
    const items = [{ price: 60 }, { price: 60 }]; // total = 120
    expect(calculateTotal(items)).toBe(108); // 120 * 0.90
  });
});

Running this now produces: Cannot find module './calculateTotal' — correct RED state.


2. GREEN Phase — Make the Test Pass

Write the minimum code needed to pass the test. Don't add anything extra.

js
// calculateTotal.js
function calculateTotal(items) {
  const total = items.reduce((sum, item) => sum + item.price, 0);
  return total > 100 ? total * 0.9 : total;
}
module.exports = { calculateTotal };

Run the test — it passes. GREEN achieved. Stop here; resist adding more logic.


3. REFACTOR Phase — Improve the Code

With a passing test as your safety net, clean up the implementation. Run tests after every change.

js
// calculateTotal.js — refactored for clarity
const DISCOUNT_THRESHOLD = 100;
const DISCOUNT_RATE = 0.9;

function calculateTotal(items) {
  const subtotal = items.reduce((sum, { price }) => sum + price, 0);
  return subtotal > DISCOUNT_THRESHOLD ? subtotal * DISCOUNT_RATE : subtotal;
}
module.exports = { calculateTotal };

Test still passes — GREEN maintained. Constants now communicate intent.


End-to-End Example: Adding a New Behavior

Next requirement: apply a 15% discount when total exceeds 200.

RED — write the failing test first:

js
it('should apply 15% discount when total exceeds 200', () => {
  const items = [{ price: 110 }, { price: 110 }]; // total = 220
  expect(calculateTotal(items)).toBe(187); // 220 * 0.85
});

GREEN — extend the implementation minimally:

js
function calculateTotal(items) {
  const subtotal = items.reduce((sum, { price }) => sum + price, 0);
  if (subtotal > 200) return subtotal * 0.85;
  if (subtotal > 100) return subtotal * 0.9;
  return subtotal;
}

REFACTOR — remove duplication with a tiered structure:

js
const DISCOUNT_TIERS = [
  { threshold: 200, rate: 0.85 },
  { threshold: 100, rate: 0.9 },
];

function calculateTotal(items) {
  const subtotal = items.reduce((sum, { price }) => sum + price, 0);
  const tier = DISCOUNT_TIERS.find(({ threshold }) => subtotal > threshold);
  return tier ? subtotal * tier.rate : subtotal;
}

Both tests pass — ready for the next cycle.


Workflow Steps

  1. Create or open the test file first
  2. Write ONE failing test for the smallest testable unit
  3. Implement minimally — just enough to pass
  4. Refactor if needed — while tests stay green
  5. Repeat for the next behavior

Decision Points

Write a new test when:

  • Adding a new feature or behavior
  • Fixing a bug (test the bug first, then fix it)
  • Handling an edge case discovered during implementation

Don't write a test when:

  • Pure refactoring (existing tests already cover the behavior)
  • Non-functional changes (formatting, comments)
  • Third-party library internals

Verification Checklist

  • All new code has corresponding tests
  • Tests fail when the feature is removed
  • Tests pass consistently (not flaky)
  • Code has been refactored for clarity
  • No unnecessary code was added

Common Mistakes to Avoid

  1. Writing tests after code — defeats the design benefit of TDD
  2. Writing multiple tests at once — one test drives one change
  3. Passing tests with hacks — the test should drive good design
  4. Skipping the refactor phase — technical debt accumulates
  5. Testing implementation details — test behavior, not internals

Integration with Other Skills

  • test-patterns: Patterns for structuring tests
  • anti-patterns: Common testing mistakes to avoid
  • debugging/root-cause-analysis: When tests reveal unexpected failures

Frequently asked questions

What does the Red Green Refactor AI skill do?

Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.

Why use Red Green Refactor on TypingMind?

Because you install it once and use it with any model. Red Green Refactor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Red Green Refactor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/skillkit/tree/main/packages/core/src/methodology/packs/testing/red-green-refactor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Red Green Refactor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Red Green Refactor?

As many as you like. As long as a model supports skills, you can use Red Green Refactor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Red Green Refactor AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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