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Test Driven Development

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johnku2011
test-driven-development

Use when implementing a feature or fixing a bug — TDD, red-green-refactor, failing test first, then minimal code, then refactor. Run npm test before claiming done.

Overview

Publisherjohnku2011
Repositoryboilerplates-with-ai-skills
Skill nametest-driven-development
Stars
239
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 johnku2011 on GitHub. Read the source before you install it.

Installation

Install the Test Driven Development 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.
    https://github.com/johnku2011/boilerplates-with-ai-skills/tree/main/shared/skills/test-driven-development
  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/johnku2011/boilerplates-with-ai-skills.git /tmp/boilerplates-with-ai-skills
mkdir -p .claude/skills
cp -r /tmp/boilerplates-with-ai-skills/shared/skills/test-driven-development .claude/skills/test-driven-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Driven Development 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 Test Driven Development 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 Test Driven Development 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.

Test-Driven Development

Overview

Write a failing test before production code. Keep each cycle small and verify with this project's test command (see AGENTS.md).

Core principle: If you did not watch the test fail, you do not know if it tests the right thing.

Iron law: No production code without a failing test first.

When to Use

Always:

  • New features
  • Bug fixes
  • Behavior changes

Exceptions (ask your human partner):

  • Throwaway prototypes
  • Generated code
  • Pure configuration with no runtime behavior

Red-Green-Refactor

  1. RED — Write one minimal test for one behavior. Run it; confirm it fails for the expected reason (missing feature, not a typo).
  2. GREEN — Write the smallest code that passes. Run the full suite.
  3. REFACTOR — Clean up while staying green. Do not add behavior.
  4. Repeat for the next increment.

Guidelines

  • Prefer many small tests over one large test.
  • Test observable behavior (HTTP status/body, CLI output, return values), not private implementation details.
  • Run npm test after every change; do not claim completion on a red suite.
  • Keep tests deterministic: no wall-clock timing, network, or ordering flakes.
  • For UI or native screens, unit-test pure logic under src/; verify layout on a simulator only when the change is visual.
  • Use real code in tests; mock only at unavoidable boundaries.

Verification Checklist

Before marking work complete:

  • Every new function or route has a test
  • Watched each new test fail before implementing
  • Failure was due to missing behavior, not setup errors
  • Wrote minimal code to pass each test
  • Full test suite passes
  • No new warnings in test output

If you cannot check every box, you skipped TDD — start over.

Bug Fixes

  1. Write a failing test that reproduces the bug.
  2. Fix with minimal code.
  3. Keep the test — it prevents regression.

Never fix bugs without a test.

Common Rationalizations

ExcuseReality
"Too simple to test"Simple code breaks. A test takes seconds.
"I'll test after"Tests that pass immediately prove nothing.
"Already manually tested"Manual checks are not repeatable in CI.
"Deleting code is wasteful"Unverified code is debt. Delete and rewrite with TDD.

Anti-patterns

  • Writing production code before any failing test.
  • Claiming done without running the project's test command.
  • Testing private functions instead of public behavior.
  • Mocking so heavily you test the mock, not the system.

Upstream Note

Adapted from obra/superpowers TDD workflow, trimmed for bwai boilerplates. Run bwai sync-upstream --skill test-driven-development to compare with upstream.

Frequently asked questions

What does the Test Driven Development AI skill do?

Use when implementing a feature or fixing a bug — TDD, red-green-refactor, failing test first, then minimal code, then refactor. Run npm test before claiming done.

Why use Test Driven Development on TypingMind?

Because you install it once and use it with any model. Test Driven Development 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 Test Driven Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/johnku2011/boilerplates-with-ai-skills/tree/main/shared/skills/test-driven-development. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Test Driven Development?

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 Test Driven Development?

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

Is the Test Driven Development AI skill free?

Yes. It is published on GitHub by johnku2011 under the MIT 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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