karpathy-guidelines — Claude Code skill: what it does, how to install
Guidelines that cut typical LLM coding mistakes: less overcomplication, surgical edits, verifiable outcomes.
npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a claude-codeWhat karpathy-guidelines does
The skill pins down four behaviors for the agent. First it states assumptions and interpretations out loud instead of guessing. Then it writes minimum code: no unasked features, no abstractions for one-off code, no error handling for impossible cases. Third rule — don't touch adjacent code, comments, or formatting when editing. Finally, a task becomes a verifiable success criterion, and the agent loops until the checks pass.
It is built on Andrej Karpathy's observations about LLM coding pitfalls. The text states the tradeoff plainly: the rules bias the agent toward caution, not speed, so trivial tasks need judgment over ritual.
Install it if your agent keeps bloating with abstractions or refactoring code nobody asked about. For one-line fixes it mostly just slows things down.
What it helps with
- state assumptions out loud before coding
- skip unasked-for features and abstractions
- keep edits to the lines the task actually touches
- turn a task into a verifiable goal and loop until it passes
- stop and ask when the task is ambiguous
Example prompts for the agent
- Refactor the auth module — follow karpathy-guidelines
- Apply karpathy-guidelines: add input validation, write the failing tests first
- Review this PR through karpathy-guidelines and flag overcomplicated spots
How to install karpathy-guidelines
1Skills CLI (Vercel)
npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a claude-code2Manually: put the files in place
~/.claude/skills/karpathy-guidelines/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/karpathy-guidelines/
1Skills CLI (Vercel)
npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a codex2Manually: put the files in place
.agents/skills/karpathy-guidelines/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a cursor2Manually: put the files in place
.cursor/skills/karpathy-guidelines/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install multica-ai/andrej-karpathy-skills karpathy-guidelines2Manually: put the files in place
.github/skills/karpathy-guidelines/SKILL.md1Skills CLI (Vercel)
npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a gemini-cli2Platform CLI
gemini skills install https://github.com/multica-ai/andrej-karpathy-skills --path skills/karpathy-guidelinesManually: put the files in place
.devin/skills/karpathy-guidelines/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Similar
Questions about karpathy-guidelines
How do I install karpathy-guidelines in Claude Code?
Run npx skills add multica-ai/andrej-karpathy-skills --skill karpathy-guidelines -a claude-code. The file lands in ~/.claude/skills/karpathy-guidelines/SKILL.md.
What does karpathy-guidelines do?
It gives the agent four rules: surface assumptions, keep code minimal, limit edits to what the task needs, and end with a verifiable success criterion.
How is it different from linters or code quality tools?
It works at the prompt level, not the tooling level. The agent applies these behavioral rules while coding. The rules come from Andrej Karpathy's notes on LLM coding pitfalls.
Source text
SKILL.md · MIT
---
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](https://x.com/karpathy/status/2015883857489522876) 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.
© multica-ai · MIT · multica-ai/andrej-karpathy-skills