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:
- Apply the specific sub-methodology relevant to the user's current task, or
- Guide the user to the right one from the 14 below.
The 14 Methodologies
| # | Skill Name | One-line Essence |
|---|---|---|
| 1 | karpathy-agentic-engineering |
Orchestrate agents with clear tasks and verifiable success criteria |
| 2 | karpathy-llm-wiki |
Let LLM maintain your knowledge base; you do the exploring |
| 3 | karpathy-llm-simulator |
Ask LLM to simulate expert debate, not give one opinion |
| 4 | karpathy-minimalism |
200 lines of pure Python beats 50 npm packages |
| 5 | karpathy-vibe-to-agentic |
Vibe coding raises the floor; agentic engineering raises the ceiling |
| 6 | karpathy-autoresearch |
Agent loops on git branches; you only change the prompt |
| 7 | karpathy-output-evolution |
Text → Markdown → HTML → rendered; always demand structure |
| 8 | karpathy-understanding-first |
Outsource thinking, never understanding |
| 9 | karpathy-idea-files |
Ship the Gist (abstract + criteria), not the code |
| 10 | karpathy-meta-reflection |
Monthly audit: what's atrophying, what's exploding |
| 11 | karpathy-supply-chain-hygiene |
Every dependency is an attack surface |
| 12 | karpathy-education-first |
Make everything teachable; nano-projects over monoliths |
| 13 | karpathy-system-prompt-learning |
Write strategy into the system prompt like a textbook |
| 14 | karpathy-practice-environments |
Build 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)
- 145k likes — Joins Anthropic: https://x.com/karpathy/status/2056753169888334312
- 59k likes — LLM Knowledge Bases: https://x.com/karpathy/status/2039805659525644595
- 56k likes — "Never felt this behind as a programmer": https://x.com/karpathy/status/2004607146781278521
- 46k likes — "Outsource thinking not understanding": https://x.com/karpathy/status/2049907410303865030
- 40k likes — Claude coding notes: https://x.com/karpathy/status/2015883857489522876
- 37k likes — Programming phase shift: https://x.com/karpathy/status/2026731645169185220
- 31k likes — LLM argue the opposite: https://x.com/karpathy/status/2037921699824607591
- 28k likes — AutoResearch project: https://x.com/karpathy/status/2030371219518931079
- 28k likes — litellm supply chain attack: https://x.com/karpathy/status/2036487306585268612
- 25k likes — 243 lines pure Python GPT: https://x.com/karpathy/status/2021694437152157847
- 22k likes — Agent network discussion: https://x.com/karpathy/status/2017442712388309406
- 19k likes — HTML output + I/O evolution: https://x.com/karpathy/status/2053872850101285137
- 27k likes — LLM Wiki Gist version: https://x.com/karpathy/status/2040470801506541998