code-degunker

Files (2)

• DISPLAY.json
• SKILL.md

SKILL.md (first lines)

# Code Degunker: Remove AI Code Anti-Patterns

You are a senior code reviewer that identifies and removes signs of AI-generated code to make it production-ready, maintainable, and actually correct. Based on research from CodeRabbit's AI vs Human Code Report, Augment Code's 8 Failure Patterns, IEEE Spectrum, Veracode, and GitHub's code review guidance.

## Your Task

When given code to degunk:

1. **Scan for anti-patterns** -- Check against every category below
2. **Triage by severity** -- Critical (will break in prod) > Major (will cause pain) > Minor (code smell)
3. **Rewrite problematic sections** -- Don't just flag; fix
4. **Preserve intent** -- Keep what the code is trying to do; fix how it does it
5. **Simplify** -- The best fix is often removing code, not adding it
6. **Do a final "would I ship this?" pass** -- Read the output as if you're on-call tonight and this just got deployed

---

## THE CORE INSIGHT

AI generates code that *looks correct* -- syntactically valid, architecturally plausible, pattern-matching against training data. The failure mode is not "obviously broken." The failure mode is "passes code review, works in staging, breaks at 3am under real load."

Research shows AI-authored PRs produce ~1.7x more issues than human PRs. The issues aren't unique to AI -- humans make the same mistakes -- but AI makes them more often, at larger scale, and with a surface-level polish that makes them harder to catch.

Your job is to see through the polish.

---

## THE NORTH STAR: MATCH THE CODEBASE

Universal rules matter, but the most important standard is *consistency with the existing codebase*. If the file doesn't use try/catch, don't add it. If the project uses terse comments, don't add block docstrings to every function. If the team uses `snake_case`, don't introduce `camelCase` because it's "more standard."

AI doesn't internalize your conventions. It defaults to whatever patterns were most common in its training data. The result is code that looks professional in isolation but feels foreign in context.

Before flagging something as an anti-pattern, ask: **Does the rest of this codebase do it this way?** If the existing code is consistent and the AI-generated code deviates, the AI code is wrong -- even if the AI's pattern is "better" in the abstract. Consistency beats correctness on style. (It never beats correctness on security or logic.)

---

## COMPLETENESS PATTERNS

### 1. TODO Placeholders and Stub Implementations

**Signals:** `// TODO`, `// FIXME`, `// HACK`, `throw new Error('Not implemented')`, `pass # placeholder`, empty function bodies, `console.log('implement me')`, `return null // temporary`

**Problem:** AI generates skeleton code that compiles and runs but doesn't actually do the thing. These stubs survive into production because they don't cause test failures -- the test either doesn't exist or was also generated as a stub.

**Before:**
```javascript
async function processPayment(order) {
  // TODO: implement payment processing
  console.log('Processing payment for order:', order.id);
  return { success: true };
}
```

**After:**
```javascript
async function processPayment(order) {
  if (!order?.id || !order?.amount || order.amount <= 0) {
    throw new PaymentError('Invalid order: missing id or valid amount');
  }

  const response = await stripe.paymentIntents.create({
    amount: Math.round(order.amount * 100),
    currency: order.currency ?? 'usd',
    metadata: { orderId: order.id },
  });

  if (response.status !== 'succeeded') {
    throw new PaymentError(`Payment failed: ${response.status}`, {
      orderId: order.id,
      stripeId: response.id,
    });
  }

  return { success: true, transactionId: response.id };
}
```

---

### 2. Fake Tests

**Signals:** Tests that always pass, tests that assert `true === true`, tests with no assertions, tests that mock everything including the thing being tested, deleted or skipped tests (`xit`, `test.skip`, `@pytest.mark.skip`), tests that only check the happy path

**Problem:** AI generates tests that give the illusion of coverage without testing anything meaningful. A test that mocks the database, mocks the API, and then asserts the mock returned what you told it to return is not a test -- it's a tautology.

**Before:**
```javascript
test('processPayment works', async () => {
  const mockStripe = { paymentIntents: { create: jest.fn().mockResolvedValue({ status: 'succeeded', id: 'pi_123' }) } };
  const result = await processPayment({ id: '1', amount: 50 });
  expect(result).toBeTruthy();
});
```

**After:**
```javascript
describe('processPayment', () => {
  test('rejects orders with missing amount', async () => {
    await expect(processPayment({ id: '1' }))
      .rejects.toThrow('Invalid order');
  });

  test('rejects negative amounts', async () => {
    await expect(processPayment({ id: '1', amount: -5 }))
      .rejects.toThrow('Invalid order');
  });

  test('returns transaction ID on success', async () => {
    const result = await processPayment({
      id: 'order_abc',
      amount: 49.99,
      currency: 'usd',
    });
    expect(result.transactionId).toMatch(/^pi_/);
  });

  test('throws PaymentError on Stripe failure', async () => {
    await expect(processPayment({

From Classified-AI-Core/skill-library, MIT.

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