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karpathy-guidelines

forrestchang/andrej-karpathy-skills

Behavioral guidelines to reduce common LLM coding mistakes through explicit assumptions, simplicity, surgical changes, and verifiable success criteria.

What is karpathy-guidelines?

A set of four core principles for writing better code with LLMs: think before coding by surfacing assumptions and tradeoffs, prioritize simplicity over speculative features, make surgical edits that touch only what's necessary, and define verifiable success criteria for each task. Use when writing, reviewing, or refactoring code to avoid overcomplication and miscommunication.

  • Surface assumptions and tradeoffs before implementing to avoid silent misinterpretations
  • Enforce simplicity by removing speculative features, unnecessary abstractions, and over-engineered error handling
  • Make surgical edits that touch only required code and match existing style without refactoring unrelated sections
  • Define verifiable success criteria and loop-based execution plans to enable independent task completion

How to install karpathy-guidelines

npx skills add https://github.com/forrestchang/andrej-karpathy-skills --skill karpathy-guidelines
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How to use karpathy-guidelines

  1. 1.Before writing code, explicitly state your assumptions and ask for clarification if multiple interpretations exist
  2. 2.Write the minimum code that solves the stated problem—remove speculative features, unnecessary abstractions, and over-engineered error handling
  3. 3.When editing existing code, match the existing style and only remove code your changes made unused; do not refactor unrelated sections
  4. 4.Define verifiable success criteria for the task (e.g., 'write tests for invalid inputs, then make them pass') and loop until verified

Use cases

Good for
  • Code review: catch overcomplicated implementations and unnecessary abstractions before merge
  • Refactoring: ensure changes are minimal and focused, with clear before/after verification
  • Bug fixes: transform vague problems into reproducible test cases with passing criteria
  • Feature implementation: prevent scope creep by explicitly stating what is and isn't being built
Who it's for
  • Software engineers using LLMs for code generation or refactoring
  • Code reviewers working with AI-assisted development
  • Teams seeking to reduce miscommunication and rework in LLM-assisted workflows
  • Developers who want to enforce disciplined, minimal-change coding practices

karpathy-guidelines FAQ

When should I ignore these guidelines?

For trivial tasks where speed matters more than precision, use judgment. These guidelines bias toward caution and are most valuable for complex changes, reviews, and multi-step tasks.

What if I notice unrelated dead code or style issues?

Mention them to the user but don't delete or fix them unless they're a direct result of your changes. Keep edits surgical and focused on the stated request.

How do I define good success criteria?

Transform vague goals into testable outcomes: 'Add validation' becomes 'Write tests for invalid inputs, then make them pass.' Strong criteria let you verify completion independently without constant clarification.

Should I refactor code while making my changes?

No. Match existing style and only touch what's necessary for your change. If you notice unrelated improvements, mention them but don't implement unless explicitly asked.

How do I handle multi-step tasks?

State a brief plan with each step and its verification check, e.g., '1. [Step] → verify: [check]'. This ensures clarity and lets you loop independently through completion.

Full instructions (SKILL.md)

Source of truth, from forrestchang/andrej-karpathy-skills.


name: karpathy-guidelines description: 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. license: MIT

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.