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luoling8192luoling8192/ai-coding-principles/ai-coding-discipline/SKILL.md

ai-coding-discipline

Skill171 starsChanged 8 months agoMITView on GitHub

Mandatory coding discipline rules that prevent common AI coding anti-patterns. MUST be loaded for ALL code writing, editing, reviewing, bug fixing, and testing tasks. Trigger on: writing code, editing code, fixing bugs, writing tests, implementing features, refactoring, code review, creating functions, adding error handling, debugging. This skill enforces fail-fast principles, proper error propagation, meaningful tests, and disciplined debugging workflows. Always active when Claude writes or modifies code.

3 other projects use a version of this.

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  • Other versions3 projects use a version of this.

    Word for word · 2

    1. luoling8192/skills★ 21100% same
    2. satbirbhbc-ux/ai-coding-principles★ 4100% same

    Edited · 0

    None found.

    Wrote their own version · 1

    1. AymanShams/codex-coding-os★ 1same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repositoryOne other file it gives its agents.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 10 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈10

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If it is null, will the fallback produce a correct result downstream?

    line 41
  2. Distinctive:

    If the answer to #3 is yes, throw instead of falling back.

    line 44
  3. Distinctive:

    If I delete the function body, does this test fail?

    line 120
  4. Distinctive:

    Do my assertions check concrete values, not just truthiness?

    line 122
  5. Distinctive:

    Use diverse test data: multiple amounts, edge cases, boundary values

    line 152
  6. Distinctive:

    Add property-based / fuzzy tests where appropriate

    line 153
  7. Distinctive:

    Test with values NOT in the original spec to catch lookup-table fakes

    line 154
  8. Distinctive:

    Why this matters: If you fix the code first and then write a test, you can never be sure the test would have caught the bug.

    line 171
  9. Distinctive:

    Before finalizing any code change, run through this checklist:

    line 204
  10. Distinctive:

    If any answer is "yes", revise before proceeding.

    line 215

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AI Coding Discipline

These rules override default AI coding tendencies. Follow them in ALL code you write or modify.


Rule 1: No Silent Fallbacks

Never use fallback values to mask data that should not be missing.

// FORBIDDEN — hides upstream bugs
const price = product?.price ?? 0;
const userName = user?.name || "Unknown";

// CORRECT — fail fast when data contract is violated
if (product.price == null) {
  throw new Error(`Product ${product.id} is missing price`);
}
const price = product.price;

When fallbacks ARE acceptable:

  • User-facing display with explicit design intent (e.g., avatar placeholder)
  • Optional configuration with documented defaults
  • External input parsing where absence is a valid state

Checklist before writing ??, ||, or ?.:

  1. Can this value legitimately be null/undefined at this point?
  2. If it is null, will the fallback produce a correct result downstream?
  3. Would a thrown error help me find a bug faster?

If the answer to #3 is yes, throw instead of falling back.


Rule 2: No Catch-All try/catch in Business Logic

Business logic functions must NOT wrap everything in try/catch. Let errors propagate naturally.

// FORBIDDEN — swallows all errors into a useless null
async function createOrder(data: OrderInput) {
  try {
    const user = await getUser(data.userId);
    const coupon = await validateCoupon(data.couponCode);
    const order = await saveOrder({ ...data, discount: coupon.value });
    return order;
  } catch (error) {
    console.log('Error creating order:', error);
    return null; // caller gets null, has no idea what failed
  }
}

// CORRECT — let errors bubble up, catch at the boundary
async function createOrder(data: OrderInput) {
  const user = await getUser(data.userId);
  const coupon = await validateCoupon(data.couponCode);
  const order = await saveOrder({ ...data, discount: coupon.value });
  return order;
}

// Catch ONLY at the API/controller boundary
app.post('/orders', async (req, res) => {
  try {
    const order = await createOrder(req.body);
    res.json(order);
  } catch (error) {
    logger.error('Order creation failed', { error, body: req.body });
    res.status(500).json({ error: 'Order creation failed' });
  }
});

Where try/catch IS appropriate:

  • API route handlers / controller boundaries
  • Top-level event handlers (message queues, cron jobs)
  • Operations where partial failure is expected and recovery is defined (e.g., batch processing with per-item error handling)
  • Specific, named error types you intend to handle differently

Never catch Error just to return null, undefined, false, or an empty object.


Rule 3: Tests Must Fail When Code Breaks

Every test must verify specific business outcomes. A test that passes when the core logic is deleted is worthless.

// FORBIDDEN — passes even if processOrder returns garbage
test('should process order', async () => {
  const result = await processOrder(mockOrder);
  expect(result).toBeDefined();
});

// CORRECT — verifies exact business behavior
test('should calculate total with 10% discount', async () => {
  const result = await processOrder({
    ...mockOrder,
    discount: 0.1,
  });
  expect(result.totalAmount).toBe(900);       // 1000 * 0.9
  expect(result.discountAmount).toBe(100);
  expect(result.status).toBe('confirmed');
});

Test quality checklist:

  1. If I delete the function body, does this test fail? If not, the test is useless.
  2. Am I testing behavior or just testing that "something exists"?
  3. Do my assertions check concrete values, not just truthiness?

Banned weak assertions (unless testing existence is the actual requirement):

  • toBeDefined(), toBeTruthy(), toBeFalsy() as sole assertion
  • toHaveLength(expect.any(Number))
  • expect(result).not.toBeNull() without further value checks

Rule 4: No Hardcoded Lookup-Table Implementations

Never implement business logic by hardcoding return values that match test cases.

// FORBIDDEN — fake implementation that "fits" the tests
function calculateDiscount(amount: number, level: string): number {
  if (amount === 1000 && level === 'gold') return 100;
  if (amount === 500 && level === 'silver') return 25;
  return 0;
}

// CORRECT — real logic
function calculateDiscount(amount: number, level: string): number {
  const rates: Record<string, number> = { gold: 0.1, silver: 0.05, bronze: 0.02 };
  const rate = rates[level] ?? 0;
  return amount * rate;
}

How to prevent this:

  • Use diverse test data: multiple amounts, edge cases, boundary values
  • Add property-based / fuzzy tests where appropriate
  • Test with values NOT in the original spec to catch lookup-table fakes

Rule 5: Red-Green Testing (TDD Order)

When fixing a bug, always write the failing test FIRST, then fix the code.

Correct order:
1. Discover bug
2. Write a test that reproduces the bug
3. Run the test — confirm it FAILS (red)
4. Fix the code
5. Run the test — confirm it PASSES (green)

Why this matters: If you fix the code first and then write a test, you can never be sure the test would have caught the bug. The test might pass for the wrong reason.

Never skip step 3. Seeing the test go from red to green is the proof that the test is valid.


Rule 6: Never Remove Debug Logs During a Fix

When debugging, debug logs are removed ONLY after the human confirms the fix works.

FORBIDDEN workflow:
1. Human asks to add debug logs
2. AI adds logs
3. Human runs code, shares log output
4. AI "finds the problem", applies fix AND removes debug logs in the same edit
5. Fix doesn't work — logs are gone, must re-add them

CORRECT workflow:
1. Human asks to add debug logs
2. AI adds logs
3. Human runs code, shares log output
4. AI applies fix ONLY — debug logs stay untouched
5. Human verifies the fix
6. Human decides when to remove debug logs (or asks AI to remove them)

Rule: Never touch debug/diagnostic logs in the same commit as a fix. They are separate concerns.


Quick Reference: Self-Check Before Submitting Code

Before finalizing any code change, run through this checklist:

CheckQuestion
FallbacksDid I use ?? or || to hide a value that should never be missing?
Error handlingDid I add try/catch in business logic that should just let errors propagate?
Test strengthWould my tests still pass if I deleted the implementation?
Test honestyDid I hardcode values to match test cases instead of implementing real logic?
TDD orderFor bug fixes: did I see the test fail before applying the fix?
Debug logsAm I removing diagnostic logs in the same change as the fix?

If any answer is "yes", revise before proceeding.

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