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Drill Tdd

Community
kucherenko
drill-tdd

Use when implementing any feature or bugfix — enforces the Red-Green-Refactor drill with no production code allowed without a failing test first

Overview

Publisherkucherenko
Repositorygangsta
Skill namedrill-tdd
Stars
86
Forks
10
Bundled files
Instructions only
LicenseMIT
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 kucherenko on GitHub. Read the source before you install it.

Installation

Install the Drill Tdd 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/kucherenko/gangsta/tree/master/skills/drill-tdd
  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/kucherenko/gangsta.git /tmp/gangsta
mkdir -p .claude/skills
cp -r /tmp/gangsta/skills/drill-tdd .claude/skills/drill-tdd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Drill Tdd 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 Drill Tdd 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 Drill Tdd 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.

The Drill: Test-Driven Development

Overview

Write the test first. Watch it fail. Write minimal code to pass.

Core principle: If you didn't watch the test fail, you don't know if it tests the right thing.

Violating the letter of the rules is violating the spirit of the rules.

When to Use

Always:

  • New features
  • Bug fixes
  • Refactoring
  • Behavior changes

Exceptions (ask the Don):

  • Throwaway prototypes
  • Generated code
  • Configuration files

Thinking "skip the Drill just this once"? Stop. That's rationalization.

The Iron Law (The Omerta of Testing)

NO PRODUCTION CODE WITHOUT A FAILING TEST FIRST

Write code before the test? Delete it. Start over.

No exceptions:

  • Don't keep it as "reference"
  • Don't "adapt" it while writing tests
  • Don't look at it
  • Delete means delete

Implement fresh from tests. Period.

Red-Green-Refactor

dot
digraph tdd_cycle {
    rankdir=LR;
    red [label="RED\nWrite failing test", shape=box, style=filled, fillcolor="#ffcccc"];
    verify_red [label="Verify fails\ncorrectly", shape=diamond];
    green [label="GREEN\nMinimal code", shape=box, style=filled, fillcolor="#ccffcc"];
    verify_green [label="Verify passes\nAll green", shape=diamond];
    refactor [label="REFACTOR\nClean up", shape=box, style=filled, fillcolor="#ccccff"];
    next [label="Next", shape=ellipse];

    red -> verify_red;
    verify_red -> green [label="yes"];
    verify_red -> red [label="wrong\nfailure"];
    green -> verify_green;
    verify_green -> refactor [label="yes"];
    verify_green -> green [label="no"];
    refactor -> verify_green [label="stay\ngreen"];
    verify_green -> next;
    next -> red;
}

RED — Write Failing Test

Write one minimal test showing what should happen.

const result = await retryOperation(operation);

expect(result).toBe('success'); expect(attempts).toBe(3); });

Clear name, tests real behavior, one thing
</Good>

<Bad>
```typescript
test('retry works', async () => {
  const mock = jest.fn()
    .mockRejectedValueOnce(new Error())
    .mockRejectedValueOnce(new Error())
    .mockResolvedValueOnce('success');
  await retryOperation(mock);
  expect(mock).toHaveBeenCalledTimes(3);
});

Vague name, tests mock not code

Requirements:

  • One behavior per test
  • Clear descriptive name
  • Real code (no mocks unless unavoidable)

Verify RED — Watch It Fail

MANDATORY. Never skip.

Run the test. Confirm:

  • Test fails (not errors)
  • Failure message is expected
  • Fails because feature missing (not typos)

Test passes? You're testing existing behavior. Fix the test.

Test errors? Fix the error, re-run until it fails correctly.

GREEN — Minimal Code

Write the simplest code to pass the test.

Don't add features, refactor other code, or "improve" beyond the test.

Verify GREEN — Watch It Pass

MANDATORY.

Run the test. Confirm:

  • Test passes
  • Other tests still pass
  • Output pristine (no errors, warnings)

Test fails? Fix code, not test.

Other tests fail? Fix now.

REFACTOR — Clean Up

After green only:

  • Remove duplication
  • Improve names
  • Extract helpers

Keep tests green. Don't add behavior.

Repeat

Next failing test for next behavior.

Good Tests

QualityGoodBad
MinimalOne thing. "and" in name? Split it.test('validates email and domain and whitespace')
ClearName describes behaviortest('test1')
Shows intentDemonstrates desired APIObscures what code should do

Why Order Matters

"I'll write tests after to verify it works"

Tests written after code pass immediately. Passing immediately proves nothing:

  • Might test wrong thing
  • Might test implementation, not behavior
  • Might miss edge cases you forgot
  • You never saw it catch the bug

Test-first forces you to see the test fail, proving it actually tests something.

"Deleting X hours of work is wasteful"

Sunk cost fallacy. The time is already gone. Your choice now:

  • Delete and rewrite with TDD (high confidence)
  • Keep it and add tests after (low confidence, likely bugs)

The "waste" is keeping code you can't trust.

"TDD is dogmatic, being pragmatic means adapting"

TDD IS pragmatic:

  • Finds bugs before commit (faster than debugging after)
  • Prevents regressions (tests catch breaks immediately)
  • Documents behavior (tests show how to use code)
  • Enables refactoring (change freely, tests catch breaks)

"Pragmatic" shortcuts = debugging in production = slower.

Common Rationalizations

ExcuseReality
"Too simple to test"Simple code breaks. Test takes 30 seconds.
"I'll test after"Tests passing immediately prove nothing.
"Already manually tested"Ad-hoc ≠ systematic. No record, can't re-run.
"Deleting X hours is wasteful"Sunk cost fallacy. Keeping unverified code is debt.
"Keep as reference, write tests first"You'll adapt it. That's testing after. Delete means delete.
"Need to explore first"Fine. Throw away exploration, start with the Drill.
"Test hard = design unclear"Listen to test. Hard to test = hard to use.
"TDD will slow me down"The Drill is faster than debugging.
"Existing code has no tests"You're improving it. Add tests for what you touch.

Red Flags — STOP and Start Over

  • Code before test
  • Test after implementation
  • Test passes immediately
  • Can't explain why test failed
  • Tests added "later"
  • Rationalizing "just this once"
  • "Keep as reference" or "adapt existing code"
  • "Already spent X hours, deleting is wasteful"
  • "TDD is dogmatic, I'm being pragmatic"

All of these mean: Delete code. Start over with the Drill.

Example: Bug Fix

Bug: Empty email accepted

RED

typescript
test('rejects empty email', async () => {
  const result = await submitForm({ email: '' });
  expect(result.error).toBe('Email required');
});

Verify RED

$ npm test
FAIL: expected 'Email required', got undefined

GREEN

typescript
function submitForm(data: FormData) {
  if (!data.email?.trim()) {
    return { error: 'Email required' };
  }
  // ...existing code
}

Verify GREEN

$ npm test
PASS

REFACTOR — Extract validation for multiple fields if needed.

Verification Checklist

Before marking work complete:

  • Every new function/method has a test
  • Watched each test fail before implementing
  • Each test failed for expected reason (feature missing, not typo)
  • Wrote minimal code to pass each test
  • All tests pass
  • Output pristine (no errors, warnings)
  • Tests use real code (mocks only if unavoidable)
  • Edge cases and errors covered

Can't check all boxes? You skipped the Drill. Start over.

Debugging Integration

Bug found? Write failing test reproducing it. Follow the Drill cycle. Test proves fix and prevents regression.

Never fix bugs without a test. Use gangsta:interrogation-debugging for root-cause investigation first.

When Stuck

ProblemSolution
Don't know how to testWrite wished-for API. Write assertion first. Ask the Don.
Test too complicatedDesign too complicated. Simplify interface.
Must mock everythingCode too coupled. Use dependency injection.
Test setup hugeExtract helpers. Still complex? Simplify design.

Omerta Compliance

  • Rule of Truth: Test output is actual command output, not claims
  • Spec is Law: Every implementation traces to a test that traces to a requirement

Frequently asked questions

What does the Drill Tdd AI skill do?

Use when implementing any feature or bugfix — enforces the Red-Green-Refactor drill with no production code allowed without a failing test first

Why use Drill Tdd on TypingMind?

Because you install it once and use it with any model. Drill Tdd 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 Drill Tdd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kucherenko/gangsta/tree/master/skills/drill-tdd. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Drill Tdd?

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 Drill Tdd?

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

Is the Drill Tdd AI skill free?

Yes. It is published on GitHub by kucherenko 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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