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karpathy-methodology

karpathy-methodology

github.com/learnpromptAgent workflows118139 installsUpdated 16w ago
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About karpathy-methodology

A master-index skill for applying or selecting among 14 methodologies distilled from Andrej Karpathy's posts and materials. It maps coding, research, decision-making, product design, and ML tasks to relevant approaches.

Key features

  1. 01Index of 14 methodologies
  2. 02Task-based method selection guide
  3. 03Master prompt with core constraints
  4. 04Guidance for coding, research, and decisions
  5. 05Methods for product design and ML research

Use cases

  1. 01Choosing an approach for an agent-assisted coding task
  2. 02Selecting methods for research or learning
  3. 03Structuring a decision or product design process

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/learnprompt/andrej-karpathy-skills --skill karpathy-methodology

After installing, type/karpathy-methodologyin Claude Code to confirm it loaded.

Bundled filesreferences/index.md

Details

From the project README

Karpathy Methodology — 14 Core Skills

Distilled from Andrej Karpathy's most-liked posts on X (2023–2026), his LLM Wiki Gist, and the multica-ai/andrej-karpathy-skills repo. Source: https://x.com/karpathy | https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

This skill is the master index. When invoked, either:

  1. Apply the specific sub-methodology relevant to the user's current task, or
  2. Guide the user to the right one from the 14 below.

The 14 Methodologies

#Skill NameOne-line Essence
1karpathy-agentic-engineeringOrchestrate agents with clear tasks and verifiable success criteria
2karpathy-llm-wikiLet LLM maintain your knowledge base; you do the exploring
3karpathy-llm-simulatorAsk LLM to simulate expert debate, not give one opinion
4karpathy-minimalism200 lines of pure Python beats 50 npm packages
5karpathy-vibe-to-agenticVibe coding raises the floor; agentic engineering raises the ceiling
6karpathy-autoresearchAgent loops on git branches; you only change the prompt
7karpathy-output-evolutionText → Markdown → HTML → rendered; always demand structure
8karpathy-understanding-firstOutsource thinking, never understanding
9karpathy-idea-filesShip the Gist (abstract + criteria), not the code
10karpathy-meta-reflectionMonthly audit: what's atrophying, what's exploding
11karpathy-supply-chain-hygieneEvery dependency is an attack surface
12karpathy-education-firstMake everything teachable; nano-projects over monoliths
13karpathy-system-prompt-learningWrite strategy into the system prompt like a textbook
14karpathy-practice-environmentsBuild gyms where agents can try, fail, and learn

Meta-Principle (the one that unifies all 14)

"You can outsource your thinking but you cannot outsource your understanding." — Karpathy, ~46k likes, 2026

Use AI to go faster. Use your brain to know if you're going in the right direction.

How to Apply This Skill

Quick Decision Tree

"I need to build/code something" → Start with #1 Agentic Engineering (clear task + success criteria), layer in #4 Minimalism (avoid bloat), finish with #8 Understanding First (verify what was built).

"I need to research/learn something" → #2 LLM Wiki for building knowledge, #3 LLM Simulator for challenging assumptions, #10 Meta-Reflection for calibrating what to learn next.

"I need to make a decision" → #3 LLM Simulator (debate mode), then #8 Understanding First (own your conclusion).

"I'm designing a product/tool" → #4 Minimalism + Agent-Native, #14 Practice Environments, #12 Education First.

"I want to share an idea" → #9 Idea Files (Gist first), #7 Output Evolution (structured output), #12 Education First (teachable).

"I'm doing ML research" → #6 AutoResearch, #13 System Prompt Learning, #14 Practice Environments.

Master Prompt Template

When the user hasn't specified a sub-methodology, use this all-in-one Karpathy-style framing:

You are operating under the Karpathy Methodology.

Core constraints:
1. UNDERSTAND before outsourcing — never ship what you can't explain
2. MINIMIZE dependencies — prefer 200-line pure implementations
3. AGENT-NATIVE outputs — CLI-friendly, markdown-structured, LLM-legible
4. VERIFIABLE goals — every task must have a testable success criterion
5. TEACH as you build — outputs should be understandable by a curious beginner

Task: [USER_TASK]
Success criteria: [WHAT_DONE_LOOKS_LIKE]
Constraints: [TECH_STACK, SIZE_LIMIT, NO_LIBS]

Source Posts (Top by Likes)

  1. 145k likes — Joins Anthropic: https://x.com/karpathy/status/2056753169888334312
  2. 59k likes — LLM Knowledge Bases: https://x.com/karpathy/status/2039805659525644595
  3. 56k likes — "Never felt this behind as a programmer": https://x.com/karpathy/status/2004607146781278521
  4. 46k likes — "Outsource thinking not understanding": https://x.com/karpathy/status/2049907410303865030
  5. 40k likes — Claude coding notes: https://x.com/karpathy/status/2015883857489522876
  6. 37k likes — Programming phase shift: https://x.com/karpathy/status/2026731645169185220
  7. 31k likes — LLM argue the opposite: https://x.com/karpathy/status/2037921699824607591
  8. 28k likes — AutoResearch project: https://x.com/karpathy/status/2030371219518931079
  9. 28k likes — litellm supply chain attack: https://x.com/karpathy/status/2036487306585268612
  10. 25k likes — 243 lines pure Python GPT: https://x.com/karpathy/status/2021694437152157847
  11. 22k likes — Agent network discussion: https://x.com/karpathy/status/2017442712388309406
  12. 19k likes — HTML output + I/O evolution: https://x.com/karpathy/status/2053872850101285137
  13. 27k likes — LLM Wiki Gist version: https://x.com/karpathy/status/2040470801506541998

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