multica-ai/andrej-karpathy-skillsPublic

A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.

AI summary: A collection of curated AI prompts and configurations modeled after Andrej Karpathy's specific workflows and techniques.

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No licenseCreated Jan 27, 2026Last push 5mo ago+1.3K stars this week+6.7K this month

Quick answers

What is andrej-karpathy-skills?
A collection of curated AI prompts and configurations modeled after Andrej Karpathy's specific workflows and techniques.
What does andrej-karpathy-skills do?
This repository is a curated collection of agent skills explicitly designed to replicate the advanced coding and learning workflows demonstrated by Andrej Karpathy. It packages his distinct, highly effective approaches to interacting with Large Language Models into structured, reusable prompts. The skills cover everything from deep-dive technical research and algorithmic problem-solving to meticulous code review practices. By installing these skills into compatible AI agents, users can automatically guide their interactions to follow rigorous analytical thinking and iterative refinement. This allows developers to dramatically improve the quality and depth of the code generated by their personal AI assistants.
Who is andrej-karpathy-skills for?
Machine learning engineers, researchers, and advanced developers who want to elevate their AI interactions by employing the exact methodologies used by top industry experts.
How do I get started with andrej-karpathy-skills?
npx skills add multica-ai/andrej-karpathy-skills -g
How popular is andrej-karpathy-skills on GitHub?
multica-ai/andrej-karpathy-skills has 216,846 stars and 21,836 forks on GitHub, and gained 1,307 stars in the last 7 days.

Star history

since Jul 29, 2026
0100K200KJul 2026Aug 2026Sep 2026Oct 2026
216.8K stars as of Oct 4, 2026. Measured daily since Jul 29, 2026; GitHub no longer exposes earlier star timestamps.

Update history

1 recorded
  • Oct 4, 2026Previously tracked as forrestchang/andrej-karpathy-skills; its 1 daily snapshot and 41 trending appearances were merged into this profile. Stars: 197,413 on 2026-07-29 under the old name, 216,810 on 2026-10-04 (+19,397).

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Signals and awards

derived from tracked data
  • Landmark project

    216,846 stars

  • Repeat trending

    94 trending appearances

  • Top 10% tracked

    Rank 16 of 1135

What andrej-karpathy-skills does

This repository is a curated collection of agent skills explicitly designed to replicate the advanced coding and learning workflows demonstrated by Andrej Karpathy. It packages his distinct, highly effective approaches to interacting with Large Language Models into structured, reusable prompts. The skills cover everything from deep-dive technical research and algorithmic problem-solving to meticulous code review practices. By installing these skills into compatible AI agents, users can automatically guide their interactions to follow rigorous analytical thinking and iterative refinement. This allows developers to dramatically improve the quality and depth of the code generated by their personal AI assistants.

Machine learning engineers, researchers, and advanced developers who want to elevate their AI interactions by employing the exact methodologies used by top industry experts.

  • Workflow emulation: Replicates the exact analytical thinking and prompting patterns used by a leading AI researcher.
  • Structured problem-solving: Enforces step-by-step reasoning protocols before the LLM generates complex algorithms or code.
  • Iterative refinement instructions: Guides the agent to continuously review, criticize, and optimize its own outputs.
  • Deep technical learning: Structures prompts to dissect complex concepts into foundational, easily understandable principles.
  • Cross-agent compatibility: Designed to be easily imported and utilized across various compliant AI assistant platforms.

Where teams use it

Advanced Algorithmic Design

Using the specialized problem-solving skill to force the LLM to architect complex machine learning algorithms from scratch.

Rigorous Code Review

Applying the iterative refinement skill to an existing codebase to identify deep architectural flaws and optimization targets.

Deep Concept Learning

Leveraging the technical learning skill to have the agent break down complex whitepapers into fundamental mental models.

Agent Customization

Integrating these advanced workflow configurations into personal AI assistants to immediately elevate their baseline capabilities.

Getting started: npx skills add multica-ai/andrej-karpathy-skills -g

README

main branch

Karpathy-Inspired Claude Code Guidelines

Check out my new project Multica — an open-source platform for running and managing coding agents with reusable skills.

Follow me on X: https://x.com/jiayuan_jy

A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.

English | 简体中文

The Problems

From Andrej's post:

"The models make wrong assumptions on your behalf and just run along with them without checking. They don't manage their confusion, don't seek clarifications, don't surface inconsistencies, don't present tradeoffs, don't push back when they should."

"They really like to overcomplicate code and APIs, bloat abstractions, don't clean up dead code... implement a bloated construction over 1000 lines when 100 would do."

"They still sometimes change/remove comments and code they don't sufficiently understand as side effects, even if orthogonal to the task."

The Solution

Four principles in one file that directly address these issues:

Principle Addresses
Think Before Coding Wrong assumptions, hidden confusion, missing tradeoffs
Simplicity First Overcomplication, bloated abstractions
Surgical Changes Orthogonal edits, touching code you shouldn't
Goal-Driven Execution Leverage through tests-first, verifiable success criteria

The Four Principles in Detail

1. Think Before Coding

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

LLMs often pick an interpretation silently and run with it. This principle forces explicit reasoning:

  • State assumptions explicitly — If uncertain, ask rather than guess
  • Present multiple interpretations — Don't pick silently when ambiguity exists
  • Push back when warranted — If a simpler approach exists, say so
  • Stop when confused — Name what's unclear and ask for clarification

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

Combat the tendency toward overengineering:

  • 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 200 lines could be 50, rewrite it

The test: 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 imperative tasks into verifiable goals:

Instead of... Transform to...
"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 the LLM loop independently. Weak criteria ("make it work") require constant clarification.

Install

Option A: Claude Code Plugin (recommended)

From within Claude Code, first add the marketplace:

/plugin marketplace add forrestchang/andrej-karpathy-skills

Then install the plugin:

/plugin install andrej-karpathy-skills@karpathy-skills

This installs the guidelines as a Claude Code plugin, making the skill available across all your projects.

Option B: CLAUDE.md (per-project)

New project:

curl -o CLAUDE.md https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md

Existing project (append):

echo "" >> CLAUDE.md
curl https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md >> CLAUDE.md

Using with Cursor

This repository includes a committed Cursor project rule (.cursor/rules/karpathy-guidelines.mdc) so the same guidelines apply when you open the project in Cursor. See CURSOR.md for setup, using the rule in other projects, and how this relates to Claude Code.

Key Insight

From Andrej:

"LLMs are exceptionally good at looping until they meet specific goals... Don't tell it what to do, give it success criteria and watch it go."

The "Goal-Driven Execution" principle captures this: transform imperative instructions into declarative goals with verification loops.

How to Know It's Working

These guidelines are working if you see:

  • Fewer unnecessary changes in diffs — Only requested changes appear
  • Fewer rewrites due to overcomplication — Code is simple the first time
  • Clarifying questions come before implementation — Not after mistakes
  • Clean, minimal PRs — No drive-by refactoring or "improvements"

Customization

These guidelines are designed to be merged with project-specific instructions. Add them to your existing CLAUDE.md or create a new one.

For project-specific rules, add sections like:

## Project-Specific Guidelines

- Use TypeScript strict mode
- All API endpoints must have tests
- Follow the existing error handling patterns in `src/utils/errors.ts`

Tradeoff Note

These guidelines bias toward caution over speed. For trivial tasks (simple typo fixes, obvious one-liners), use judgment — not every change needs the full rigor.

The goal is reducing costly mistakes on non-trivial work, not slowing down simple tasks.

License

MIT

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Recent activity

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When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 1 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 0 commitsSun 12:00 — 0 commitsSun 13:00 — 0 commitsSun 14:00 — 0 commitsSun 15:00 — 0 commitsSun 16:00 — 0 commitsSun 17:00 — 0 commitsSun 18:00 — 0 commitsSun 19:00 — 0 commitsSun 20:00 — 0 commitsSun 21:00 — 0 commitsSun 22:00 — 0 commitsSun 23:00 — 0 commitsMon 0:00 — 0 commitsMon 1:00 — 0 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 0 commitsMon 9:00 — 0 commitsMon 10:00 — 0 commitsMon 11:00 — 0 commitsMon 12:00 — 0 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 0 commitsMon 16:00 — 0 commitsMon 17:00 — 0 commitsMon 18:00 — 1 commitsMon 19:00 — 0 commitsMon 20:00 — 0 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 0 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 1 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 1 commitsTue 9:00 — 0 commitsTue 10:00 — 0 commitsTue 11:00 — 2 commitsTue 12:00 — 1 commitsTue 13:00 — 0 commitsTue 14:00 — 0 commitsTue 15:00 — 0 commitsTue 16:00 — 0 commitsTue 17:00 — 0 commitsTue 18:00 — 0 commitsTue 19:00 — 0 commitsTue 20:00 — 0 commitsTue 21:00 — 0 commitsTue 22:00 — 1 commitsTue 23:00 — 0 commitsWed 0:00 — 0 commitsWed 1:00 — 1 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 0 commitsWed 9:00 — 0 commitsWed 10:00 — 0 commitsWed 11:00 — 0 commitsWed 12:00 — 0 commitsWed 13:00 — 0 commitsWed 14:00 — 0 commitsWed 15:00 — 0 commitsWed 16:00 — 0 commitsWed 17:00 — 0 commitsWed 18:00 — 0 commitsWed 19:00 — 0 commitsWed 20:00 — 0 commitsWed 21:00 — 0 commitsWed 22:00 — 0 commitsWed 23:00 — 0 commitsThu 0:00 — 1 commitsThu 1:00 — 2 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 0 commitsThu 10:00 — 0 commitsThu 11:00 — 0 commitsThu 12:00 — 0 commitsThu 13:00 — 0 commitsThu 14:00 — 0 commitsThu 15:00 — 0 commitsThu 16:00 — 0 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 0 commitsThu 20:00 — 0 commitsThu 21:00 — 1 commitsThu 22:00 — 0 commitsThu 23:00 — 0 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 0 commitsFri 9:00 — 0 commitsFri 10:00 — 0 commitsFri 11:00 — 0 commitsFri 12:00 — 0 commitsFri 13:00 — 0 commitsFri 14:00 — 0 commitsFri 15:00 — 0 commitsFri 16:00 — 1 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 0 commitsFri 20:00 — 0 commitsFri 21:00 — 0 commitsFri 22:00 — 0 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 0 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 2 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 1 commitsSat 16:00 — 0 commitsSat 17:00 — 0 commitsSat 18:00 — 0 commitsSat 19:00 — 0 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 3 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Sep 9, 2026daily#5+325
Sep 8, 2026daily#5+325
Aug 27, 2026daily#9+830
Aug 26, 2026daily#9+830
Aug 25, 2026daily#3+588
Aug 24, 2026daily#7+315
Aug 23, 2026daily#7+315
Jul 27, 2026daily#18+3
Jul 24, 2026daily#20+4
Jul 17, 2026daily#9+8
Jul 16, 2026daily#23+4
Jul 15, 2026daily#22+5
Jul 14, 2026daily#11+5
Jul 13, 2026daily#21+3
Jul 9, 2026daily#9+5
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