Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Instruções da origem · Visualização somente leitura
name
tdd
description
Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.
Test-Driven Development
Philosophy
Core principle: Tests should verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't.
Good tests are integration-style: they exercise real code paths through public APIs. They describe what the system does, not how it does it. A good test reads like a specification - "user can checkout with valid cart" tells you exactly what capability exists. These tests survive refactors because they don't care about internal structure.
// GOOD: Tests observable behaviortest("user can checkout with valid cart", async () => {
const cart = createCart();
cart.add(product);
const result = awaitcheckout(cart, paymentMethod);
expect(result.status).toBe("confirmed");
});
// GOOD: Verifies through interfacetest("createUser makes user retrievable", async () => {
const user = await ({ : });
retrieved = (user.);
(retrieved.).();
});
createUser
name
"Alice"
const
await
getUser
id
expect
name
toBe
"Alice"
Characteristics of good tests:
Tests behavior users/callers care about
Uses public API only
Survives internal refactors
Describes WHAT, not HOW
One logical assertion per test
Bad tests are coupled to implementation. They mock internal collaborators, test private methods, or verify through external means (like querying a database directly instead of using the interface). The warning sign: your test breaks when you refactor, but behavior hasn't changed.
Test breaks when refactoring without behavior change
Test name describes HOW not WHAT
Prefer writing tests before implementation. If you've already written code, consider starting fresh from tests rather than retrofitting — tests written after tend to verify what you built, not what's required.
Mocking
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
Use dependency injection — pass external dependencies in rather than creating them internally:
// Easy to mockfunctionprocessPayment(order, paymentClient) {
return paymentClient.charge(order.total);
}
// Hard to mockfunctionprocessPayment(order) {
const client = newStripeClient(process.env.STRIPE_KEY);
return client.charge(order.total);
}
Prefer SDK-style interfaces — specific functions for each external operation:
// GOOD: Each function is independently mockableconst 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 mockconst api = {
fetch: (endpoint, options) =>fetch(endpoint, options),
};
Interface Design for Testability
Accept dependencies, don't create them
// TestablefunctionprocessOrder(order, paymentGateway) {}
// Hard to testfunctionprocessOrder(order) {
const gateway = newStripeGateway();
}
Return results, don't produce side effects
// TestablefunctioncalculateDiscount(cart): Discount {}
// Hard to testfunctionapplyDiscount(cart): void {
cart.total -= discount;
}
Small surface area — fewer methods = fewer tests needed, fewer params = simpler test setup
Deep modules (from "A Philosophy of Software Design"): small interface + lots of implementation. When designing, ask: Can I reduce methods? Simplify params? Hide more complexity inside?
Anti-Pattern: Horizontal Slices
DO NOT write all tests first, then all implementation. This is "horizontal slicing" - treating RED as "write all tests" and GREEN as "write all code."
This produces crap tests:
Tests written in bulk test imagined behavior, not actual behavior
You end up testing the shape of things (data structures, function signatures) rather than user-facing behavior
Tests become insensitive to real changes - they pass when behavior breaks, fail when behavior is fine
You outrun your headlights, committing to test structure before understanding the implementation
Correct approach: Vertical slices via tracer bullets. One test → one implementation → repeat. Each test responds to what you learned from the previous cycle.
Confirm with user what interface changes are needed
Confirm with user which behaviors to test (prioritize)
Identify opportunities for deep modules (small interface, deep implementation)
Design interfaces for testability
List the behaviors to test (not implementation steps)
Get user approval on the plan
Ask: "What should the public interface look like? Which behaviors are most important to test?"
You can't test everything. Confirm with the user exactly which behaviors matter most. Focus testing effort on critical paths and complex logic, not every possible edge case.
2. Tracer Bullet
Write ONE test that confirms ONE thing about the system:
RED: Write test → run test → confirm it FAILS correctly
GREEN: Write minimal code → run test → confirm it PASSES
Test passes immediately? You're testing existing behavior. Fix the test.
Test errors (not assertion failure)? Fix the error first — erroring is not the same as failing.
This is your tracer bullet - proves the path works end-to-end.
3. Incremental Loop
For each remaining behavior:
RED: Write next test → run test → confirm it FAILS correctly
GREEN: Write minimal code → run test → confirm it PASSES
Rules:
One test at a time
Only enough code to pass current test
Don't anticipate future tests
Keep tests focused on observable behavior
4. Refactor
After all tests pass, look for refactor candidates:
Consider what new code reveals about existing code
Run tests after each refactor step
Refactor candidates: duplication → extract function/class, long methods → break into private helpers, shallow modules → combine or deepen, feature envy → move logic to where data lives, primitive obsession → introduce value objects.
Never refactor while RED. Get to GREEN first.
Checklist Per Cycle
[ ] Test describes behavior, not implementation
[ ] Test uses public interface only
[ ] Test would survive internal refactor
[ ] Code is minimal for this test
[ ] No speculative features added
[ ] Watched test fail before writing code
[ ] Failure was for expected reason (missing feature, not typo)
[ ] All other tests still pass
Bug Fix Example
TDD applies to bug fixes — write a test that reproduces the bug first.
# Bug: empty email passes validation
RED: test("rejects empty email", () => {
const result = validateEmail("");
expect(result.valid).toBe(false);
});
→ Run test → FAILS (empty string passes validation) ✓
GREEN: Add check: if (!email || !email.includes("@")) return { valid: false }
→ Run test → PASSES ✓
Verify all other validation tests still pass.
Type Testing
Compile-time type assertions. No runtime — just bun typecheck. Catches regressions in generics, conditional types, and type constraints that runtime tests can't see.
import { Expect, Equal, Not, IsAny } from"./utils";
// Block scope each test to avoid name collisions
{
typeResult = ReturnType<typeof myGenericFn<SomeInput>>;
Expect<Equal<Result, { id: string; name: string }>>;
Expect<Not<IsAny<Result>>>;
}
Negative Tests
@ts-expect-errormust be on the line immediately before the error. Always include a reason. Unused directive = failing test (constraint is missing).
// ✅ directive on line immediately before errordoSomething({
// @ts-expect-error - name must be stringname: 123,
});
// ❌ directive too far from errordoSomething({
// @ts-expect-error - name must be string
...defaults,
name: 123,
});
Tips
declare const for mock values without runtime: declare const ctx: SomeCtx;
type _name = Expect<...> when you need a type-level-only assertion (no runtime Expect() call needed)
/* biome-ignore-all lint */ at file top for type-only files — suppresses unused variable warnings
Run with bun typecheck. If it compiles, it passes.
