test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
- License 许可
- MIT
- 最近更新
- 2026-03-28
- Stars
- ★ 0
- Forks
- 2
01 前言 Front Matter
- name
- test-driven-development
- description
- Use when implementing any feature or bugfix, before writing implementation code
02 资源文件 1
- 参考文档testing-anti-patterns.md8.1 KB
03 SKILL.md 正文
Test-Driven Development (TDD)
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 your human partner):
- Throwaway prototypes
- Generated code
- Configuration files
Thinking "skip TDD just this once"? Stop. That's rationalization.
The Iron Law
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
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.
<Good>
test('retries failed operations 3 times', async () => {
let attempts = 0;
const operation = () => {
attempts++;
if (attempts < 3) throw new Error('fail');
return 'success';
};
const result = await retryOperation(operation);
expect(result).toBe('success');
expect(attempts).toBe(3);
});
Clear name, tests real behavior, one thing </Good>
<Bad>
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 </Bad>
Requirements:
- One behavior
- Clear name
- Real code (no mocks unless unavoidable)
Verify RED - Watch It Fail
MANDATORY. Never skip.
npm test path/to/test.test.ts
Confirm:
- Test fails (not errors)
- Failure message is expected
- Fails because feature missing (not typos)
Test passes? You're testing existing behavior. Fix test.
Test errors? Fix error, re-run until it fails correctly.
GREEN - Minimal Code
Write simplest code to pass the test.
<Good>
async function retryOperation<T>(fn: () => Promise<T>): Promise<T> {
for (let i = 0; i < 3; i++) {
try {
return await fn();
} catch (e) {
if (i === 2) throw e;
}
}
throw new Error('unreachable');
}
Just enough to pass </Good>
<Bad>
async function retryOperation<T>(
fn: () => Promise<T>,
options?: {
maxRetries?: number;
backoff?: 'linear' | 'exponential';
onRetry?: (attempt: number) => void;
}
): Promise<T> {
// YAGNI
}
Over-engineered </Bad>
Don't add features, refactor other code, or "improve" beyond the test.
Verify GREEN - Watch It Pass
MANDATORY.
npm test path/to/test.test.ts
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 feature.
Good Tests
| Quality | Good | Bad |
|---|---|---|
| Minimal | One thing. "and" in name? Split it. | test('validates email and domain and whitespace') |
| Clear | Name describes behavior | test('test1') |
| Shows intent | Demonstrates desired API | Obscures 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.
"I already manually tested all the edge cases"
Manual testing is ad-hoc. You think you tested everything but:
- No record of what you tested
- Can't re-run when code changes
- Easy to forget cases under pressure
- "It worked when I tried it" ≠ comprehensive
Automated tests are systematic. They run the same way every time.
"Deleting X hours of work is wasteful"
Sunk cost fallacy. The time is already gone. Your choice now:
- Delete and rewrite with TDD (X more hours, high confidence)
- Keep it and add tests after (30 min, low confidence, likely bugs)
The "waste" is keeping code you can't trust. Working code without real tests is technical debt.
"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.
"Tests after achieve the same goals - it's spirit not ritual"
No. Tests-after answer "What does this do?" Tests-first answer "What should this do?"
Tests-after are biased by your implementation. You test what you built, not what's required. You verify remembered edge cases, not discovered ones.
Tests-first force edge case discovery before implementing. Tests-after verify you remembered everything (you didn't).
30 minutes of tests after ≠ TDD. You get coverage, lose proof tests work.
Common Rationalizations
| Excuse | Reality |
|---|---|
| "Too simple to test" | Simple code breaks. Test takes 30 seconds. |
| "I'll test after" | Tests passing immediately prove nothing. |
| "Tests after achieve same goals" | Tests-after = "what does this do?" Tests-first = "what should this do?" |
| "Already manually tested" | Ad-hoc ≠ systematic. No record, can't re-run. |
| "Deleting X hours is wasteful" | Sunk cost fallacy. Keeping unverified code is technical 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 TDD. |
| "Test hard = design unclear" | Listen to test. Hard to test = hard to use. |
| "TDD will slow me down" | TDD faster than debugging. Pragmatic = test-first. |
| "Manual test faster" | Manual doesn't prove edge cases. You'll re-test every change. |
| "Existing code has no tests" | You're improving it. Add tests for existing code. |
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"
- "I already manually tested it"
- "Tests after achieve the same purpose"
- "It's about spirit not ritual"
- "Keep as reference" or "adapt existing code"
- "Already spent X hours, deleting is wasteful"
- "TDD is dogmatic, I'm being pragmatic"
- "This is different because..."
All of these mean: Delete code. Start over with TDD.
Example: Bug Fix
Bug: Empty email accepted
RED
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
function submitForm(data: FormData) {
if (!data.email?.trim()) {
return { error: 'Email required' };
}
// ...
}
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 TDD. Start over.
When Stuck
| Problem | Solution |
|---|---|
| Don't know how to test | Write wished-for API. Write assertion first. Ask your human partner. |
| Test too complicated | Design too complicated. Simplify interface. |
| Must mock everything | Code too coupled. Use dependency injection. |
| Test setup huge | Extract helpers. Still complex? Simplify design. |
Debugging Integration
Bug found? Write failing test reproducing it. Follow TDD cycle. Test proves fix and prevents regression.
Never fix bugs without a test.
Testing Anti-Patterns
When adding mocks or test utilities, read @testing-anti-patterns.md to avoid common pitfalls:
- Testing mock behavior instead of real behavior
- Adding test-only methods to production classes
- Mocking without understanding dependencies
Final Rule
Production code → test exists and failed first
Otherwise → not TDD
No exceptions without your human partner's permission.
04 同仓库更多 Skills
zapier-make-patterns
No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points. This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power
yeet
Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).
workflow-automation
Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility
trigger-dev
Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.
planning-with-files
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.
n8n-workflow-patterns
Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.