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Karpathy Methodology

  • 23 installs
  • 82 repo stars
  • Updated July 10, 2026
  • learnprompt/andrej-karpathy-skills

Helps with ai & agent building tasks.

About

karpathy-methodology is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • karpathy-methodology
  • AI & Agent Building
  • AI-coding skill

Karpathy Methodology by the numbers

  • 23 all-time installs (skills.sh)
  • +5 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #10,032 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/learnprompt/andrej-karpathy-skills --skill karpathy-methodology

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Listed on Skillselion
Installs23
repo stars82
Last updatedJuly 10, 2026
Repositorylearnprompt/andrej-karpathy-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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.

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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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