Karpathy Guidelines

作者 multica-ai2c606141936fMIT收錄於 2026年10月8日更新於 2026年10月8日

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

AI 產生的概覽

一套行為準則,引導 AI 程式代理做出更簡單、更克制、可驗證的程式碼變更。

功能
這項技能為撰寫、審查或重構程式碼的 AI 代理提供一組行為規則。內容包括:在實作前明確說明假設、讓程式碼保持精簡且不做投機性設計、只變更請求所要求的範圍,以及把任務轉化為附有成功標準的可驗證目標。它不會產出任何檔案或成品,只影響代理處理程式工作的方式。
適用情境
當代理即將撰寫、審查或重構程式碼,而風險在於過度複雜、變更超出請求範圍或成功標準不明確時使用。也適用於需要提出簡短計畫並附上驗證步驟的多步驟程式任務。
執行需求
不需要任何工具、套件、執行環境、憑證或網路存取。它僅為指示,不附帶指令碼。

Karpathy Guidelines

Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations on LLM coding pitfalls.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Before implementing:

  • State your assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them - don't pick silently.
  • If a simpler approach exists, say so. Push back when warranted.
  • If something is unclear, stop. Name what's confusing. Ask.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

  • No features beyond what was asked.
  • No abstractions for single-use code.
  • No "flexibility" or "configurability" that wasn't requested.
  • No error handling for impossible scenarios.
  • If you write 200 lines and it could be 50, rewrite it.

Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

  • Don't "improve" adjacent code, comments, or formatting.
  • Don't refactor things that aren't broken.
  • Match existing style, even if you'd do it differently.
  • If you notice unrelated dead code, mention it - don't delete it.

When your changes create orphans:

  • Remove imports/variables/functions that YOUR changes made unused.
  • Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform tasks into verifiable goals:

  • "Add validation" → "Write tests for invalid inputs, then make them pass"
  • "Fix the bug" → "Write a test that reproduces it, then make it pass"
  • "Refactor X" → "Ensure tests pass before and after"

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]2. [Step] → verify: [check]3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.

來源與署名

來源:multica-ai/andrej-karpathy-skills位於skills/karpathy-guidelines提交2c60614

授權條款: MIT

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