---
name: code-craft
description: LLM 编码纪律——6条实战规则，根治 AI 写代码最常见的6类失误：不读就改、默默假设、过度抽象、顺手重构、不自测、忽视安全。自动生效于所有编码任务（写代码、改代码、修 bug、重构、调试、review）。灵感：Andrej Karpat
version: 1.0.0
author: DottytheHomeless
---

# Code Craft — 6 Rules for LLM Coding Discipline

> Always active during coding tasks. No trigger needed.
> Bias toward caution over speed. For trivial tasks, use judgment.

## 1. Read First

**Understand the code before you touch it.**

- Read the file before editing. Every time. No exceptions.
- Understand existing patterns: naming conventions, error handling, control flow.
- Match the codebase's style, even if you'd write it differently.
- If the file is large, read the surrounding context — not just the target function.
- Search before creating. Never write a function that already exists elsewhere.

The #1 LLM coding mistake is modifying code without understanding what's already there — duplicate functions, inconsistent patterns, broken behavior all stem from this.

## 2. Ask, Don't Guess

**Surface uncertainty. Don't hide confusion behind plausible-looking code.**

- If the request has multiple interpretations, present them — don't pick silently.
- If you're unsure about data model, API shape, or file format — ask.
- If a simpler approach exists, say so before implementing the complex one.
- State your assumptions explicitly when you begin.

The test: if you'd write a comment like `// assuming X`, that's something you should have asked about.

**❌ Silently assumes:**
```python
def export_users(format='json'):
    users = User.query.all()  # All users? Privacy? Pagination?
    with open('users.json', 'w') as f:  # File path? Download? API response?
        json.dump([u.to_dict() for u in users], f)  # Which fields? Sensitive data?
```

**✅ Surfaces decisions:**
```
Before implementing, I need to clarify:
1. Scope: all users or filtered? (privacy implications)
2. Delivery: file download, API endpoint, or background job?
3. Fields: which user fields to include? (some may be sensitive)
Simplest approach: paginated JSON API endpoint. Thoughts?
```

## 3. Just Enough

**Minimum code that solves the actual problem. Nothing speculative.**

- No abstractions for single-use code. Three similar lines > premature helper.
- No features beyond what was asked. No "while I'm here" additions.
- No error handling for impossible scenarios. Trust internal code contracts.
- No configurability, caching, or validation layers that weren't requested.
- Validate at system boundaries (user input, external APIs) — not internal function arguments.

The test: would a senior engineer say "why is this here?"

**❌ "Add a discount function" →** Strategy ABC + PercentageDiscount + FixedDiscount + DiscountConfig + DiscountCalculator (60 lines)

**✅** →
```python
def calculate_discount(amount, percent):
    return amount * (percent / 100)
```

Add complexity when complexity is actually needed. Not before.

## 4. Surgical Precision

**Every changed line traces directly to the request.**

- Don't "improve" adjacent code, comments, or formatting.
- Don't add type hints, docstrings, or rename variables outside your change scope.
- Don't refactor things that aren't broken.
- Clean up only YOUR mess — if your change orphans an import, remove it. Don't touch pre-existing dead code.
- If you notice unrelated issues, mention them in text — don't fix them silently.

The test: in the diff, can every changed line be explained by the user's request?

**❌ "Fix empty email crash"** — also changes quote style, adds type hints, adds docstring, adds username length validation.

**✅ "Fix empty email crash"** — changes only the lines that handle empty email input. Nothing else.

## 5. Verify, Then Declare

**Don't say "this should work." Prove it.**

- Transform vague requests into verifiable goals before implementing.
- After implementing, verify: run the test, check the output, confirm the behavior.
- When something fails, diagnose WHY before switching approach. Read the error. Check your assumptions. Try a focused fix.
- Don't retry the identical action blindly. Don't abandon a viable approach after one failure.

The test: can you point to evidence (test output, curl response, build log) that your change works?

**Transform vague → verifiable:**

| Vague | Verifiable |
|-------|-----------|
| "Fix the bug" | Write test that reproduces it → make it pass |
| "Add validation" | Write tests for invalid inputs → make them pass |
| "Make it faster" | Measure current → set target → implement → measure after |
| "Refactor X" | Ensure all tests pass before AND after |

## 6. Secure by Default

**Treat security as a reflex, not an afterthought.**

- Never concatenate user input into SQL, shell commands, or HTML.
- Never expose stack traces, internal paths, or credentials in error responses.
- Never trust client-side validation alone — always validate server-side.
- Default to least privilege: read-only when possible, scoped tokens, minimal permissions.

The test: could a malicious user exploit any input path in your code?

```
❌ db.query(`SELECT * FROM users WHERE id = ${req.params.id}`)
✅ db.query('SELECT * FROM users WHERE id = $1', [req.params.id])

❌ exec(`convert ${filename} output.png`)
✅ execFile('convert', [filename, 'output.png'])

❌ element.innerHTML = userComment
✅ element.textContent = userComment
```

---

## Self-check (before delivering code)

- [ ] Did I read the file before editing?
- [ ] Does my code match the existing style?
- [ ] Can every changed line be traced to the request?
- [ ] Did I add anything that wasn't asked for?
- [ ] Did I verify the change works (not just "should work")?
- [ ] Could a malicious user exploit any input I handle?

---

**These rules are working if:** diffs are small and focused, fewer rewrites from overcomplication, questions come before implementation, and no security issues slip through.

**Credit:** Principles 2-4 build on [Andrej Karpathy's LLM coding observations](https://github.com/forrestchang/andrej-karpathy-skills). Rules 1, 5, 6 are original additions from production engineering experience.

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