What it does. Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests. Part of mattpocock/skills (Skills for Real Engineers) (mattpocock/skills).

Install

• npx skills add mattpocock/skills --skill tdd, or copy the skill folder into ~/.claude/skills/tdd/.
• Raw file: curl -sL https://raw.githubusercontent.com/mattpocock/skills/HEAD/skills/engineering/tdd/SKILL.md

SKILL.md (verbatim)

name: tdd
description: Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.

Test-Driven Development

TDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle: consult them before and during the loop, not after.

When exploring the codebase, read CONTEXT.md (if it exists) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.

What a good test is

Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification: "user can checkout with valid cart" tells you exactly what capability exists, and it survives refactors because it doesn't care about internal structure.

See tests.md for examples and mocking.md for mocking guidelines.

Seams: where tests go

A seam is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.

Test only at pre-agreed seams. Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything, so agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.

Ask: "What's the public interface, and which seams should we test?"

When the shape of that interface is itself in question (how deep the module is, where the seam belongs, what the interface should expose), call the Skill tool with "codebase-design" for the vocabulary. It is the shared source of the module, interface, depth, seam, adapter, leverage and locality terms, and it is a reference to consult, not a session to run.

Anti-patterns

• Implementation-coupled: mocks internal collaborators, tests private methods, or verifies through a side channel (querying the database instead of using the interface). The tell: the test breaks when you refactor but behavior hasn't changed.
• Tautological: the assertion recomputes the expected value the way the code does (expect(add(a, b)).toBe(a + b), a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth: a known-good literal, a worked example, the spec.
• Horizontal slicing: writing all tests first, then all implementation. Bulk tests verify imagined behavior: you test the shape of things rather than user-facing behavior, the tests go insensitive to real changes, and you commit to test structure before understanding the implementation. Work in vertical slices instead: one test → one implementation → repeat, each test a tracer bullet that responds to what the last cycle taught you.

Rules of the loop

• Red before green. Write the failing test first, then only enough code to pass it. Don't anticipate future tests or add speculative features.
• One slice at a time. One seam, one test, one minimal implementation per cycle.
• Refactoring is not part of the loop. It belongs to the review stage (see the code-review skill), not the red → green implementation cycle.

Other files in this skill

• agents/openai.yaml
• mocking.md
• tests.md

mocking.md (verbatim)

When to Mock

Mock at system boundaries only:

• External APIs (payment, email, etc.)
• Databases (sometimes - prefer test DB)
• Time/randomness
• File system (sometimes)

Don't mock:

• Your own classes/modules
• Internal collaborators
• Anything you control

Designing for Mockability

At system boundaries, design interfaces that are easy to mock:

1. Use dependency injection

Pass external dependencies in rather than creating them internally:

// Easy to mock
function processPayment(order, paymentClient) {
  return paymentClient.charge(order.total);
}

// Hard to mock
function processPayment(order) {
  const client = new StripeClient(process.env.STRIPE_KEY);
  return client.charge(order.total);
}

2. Prefer SDK-style interfaces over generic fetchers

Create specific functions for each external operation instead of one generic function with conditional logic:

// GOOD: Each function is independently mockable
const api = {
  getUser: (id) => fetch(`/users/${id}`),
  getOrders: (userId) => fetch(`/users/${userId}/orders`),
  createOrder: (data) => fetch('/orders', { method: 'POST', body: data }),
};

// BAD: Mocking requires conditional logic inside the mock
const api = {
  fetch: (endpoint, options) => fetch(endpoint, options),
};

The SDK approach means:

• Each mock returns one specific shape
• No conditional logic in test setup
• Easier to see which endpoints a test exercises
• Type safety per endpoint

tests.md (verbatim)

Good and Bad Tests

Good Tests

Integration-style: Test through real interfaces, not mocks of internal parts.

// GOOD: Tests observable behavior
test("user can checkout with valid cart", async () => {
  const cart = createCart();
  cart.add(product);
  const result = await checkout(cart, paymentMethod);
  expect(result.status).toBe("confirmed");
});

Characteristics:

• Tests behavior users/callers care about
• Uses public API only
• Survives internal refactors
• Describes WHAT, not HOW
• One logical assertion per test

Bad Tests

Implementation-detail tests: Coupled to internal structure.

// BAD: Tests implementation details
test("checkout calls paymentService.process", async () => {
  const mockPayment = jest.mock(paymentService);
  await checkout(cart, payment);
  expect(mockPayment.process).toHaveBeenCalledWith(cart.total);
});

Red flags:

• Mocking internal collaborators
• Testing private methods
• Asserting on call counts/order
• Test breaks when refactoring without behavior change
• Test name describes HOW not WHAT
• Verifying through external means instead of interface

// BAD: Bypasses interface to verify
test("createUser saves to database", async () => {
  await createUser({ name: "Alice" });
  const row = await db.query("SELECT * FROM users WHERE name = ?", ["Alice"]);
  expect(row).toBeDefined();
});

// GOOD: Verifies through interface
test("createUser makes user retrievable", async () => {
  const user = await createUser({ name: "Alice" });
  const retrieved = await getUser(user.id);
  expect(retrieved.name).toBe("Alice");
});

Tautological tests: Expected value restates the implementation, so the test passes by construction.

// BAD: Expected value is recomputed the way the code computes it
test("calculateTotal sums line items", () => {
  const items = [{ price: 10 }, { price: 5 }];
  const expected = items.reduce((sum, i) => sum + i.price, 0);
  expect(calculateTotal(items)).toBe(expected);
});

// GOOD: Expected value is an independent, known literal
test("calculateTotal sums line items", () => {
  expect(calculateTotal([{ price: 10 }, { price: 5 }])).toBe(15);
});

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