MCPInstall

karpathy-education-first

Apply the education-first mindset — make everything you build teachable, create nano-project explanations, write for beginners. Use this skill when the user wants to explain a project to beginners, create a tutorial from code, write documentation that teaches (not just documents), make a concept accessible, or says "make this teachable", "explain like im a beginner", "nano project version", "teaching version", "explain from scratch", "blog post about this". Based on Karpathy 243-line GPT and Eureka Labs posts.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/learnprompt/andrej-karpathy-skills --skill karpathy-education-first
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads karpathy-education-first automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Skill 12: Education-First Mindset(教育至上)

Source: https://x.com/karpathy/status/2021694437152157847 | https://x.com/karpathy/status/2056753169888334312 243-line pure Python GPT | "education热情不变" even at Anthropic

Core Principle

If you can't teach it, you don't own it. Make everything a nano-project.

Karpathy's signature move: take a complex system and reimplement it from scratch in minimal, readable code with maximal explanation. Not for production. For understanding.

The 243-line GPT wasn't the fastest implementation. It was the most comprehensible implementation. That's the goal.

The Teaching Version Prompt

After completing any project, generate its teaching version:

I just built [PROJECT/CONCEPT]. Now create a teaching version of it.

[PASTE CODE OR DESCRIBE PROJECT]

Teaching version requirements:
1. Target audience: curious beginner who knows [PREREQUISITE LEVEL]
2. Start with: "Here's what we're building and why it matters" (2 paragraphs)
3. Walk through the code line-by-line in the most important sections
4. For every non-obvious decision: add a comment explaining WHY, not just WHAT
5. Add a "Try this yourself" section at the end with 3 small exercises
6. Maximum complexity rule: if a beginner would say "wait, what?" — add an explanation

Output: the teaching version as a complete annotated script or tutorial.

The Nano-Project Pattern

Karpathy's approach to making anything understandable:

Create a nano-project that demonstrates [COMPLEX CONCEPT].

Rules:
- Under 200 lines of code (pure Python / stdlib preferred)
- Zero external dependencies
- Every line earns its place
- Annotated: inline comments explain the key insight of each section
- Complete: runs from scratch, shows meaningful output
- Pedagogical: the code's structure mirrors the concept's structure

The concept to demonstrate: [CONCEPT]
What should the reader understand after running this? [LEARNING GOAL]

Also provide:
- 3-sentence explanation at the top of the file
- What to try next (3 suggestions for extending it)

The "Explain Like Karpathy" Prompt

For explaining any technical concept:

Explain [CONCEPT] the way Karpathy would explain it — clear, direct, minimal jargon, example-first.

Format:
1. The one-sentence intuition (what it IS, not what it does)
2. The simplest possible concrete example (with actual numbers or code)
3. Why it matters (1-2 sentences, no hype)
4. The common misconception most people have about it
5. If you want to go deeper: [3 resources, ordered by accessibility]

Audience: [DESCRIBE YOUR READER]

Documentation That Teaches

Transform technical documentation from reference to tutorial:

Rewrite this documentation to be educational, not just informational.

Current docs:
[PASTE DOCUMENTATION]

Rewrite rules:
1. Lead with a concrete example, not with definitions
2. Explain each parameter with: what it does + why you'd change it + what the default is and why
3. Add a "Common patterns" section showing 3 real use cases
4. Add a "Common mistakes" section showing 3 errors people make and how to fix them
5. Keep all technical accuracy; only improve pedagogical structure
6. Add: "After reading this, you should be able to [LEARNING GOAL]" at the top

The Blog Post Generator

For turning any project into a shareable piece of teaching content:

Write a technical blog post about [PROJECT/INSIGHT].

Style: Karpathy-style — direct, precise, example-driven, opinionated.

Structure:
1. Hook: why does this matter RIGHT NOW? (1-2 punchy sentences)
2. The core insight: one thing that changes how you think about [topic]
3. Show, don't tell: working code example that demonstrates the insight
4. What I tried that didn't work (builds credibility + saves readers time)
5. What I learned: 3-5 concrete takeaways, actionable
6. What's next: 2-3 things worth exploring

Tone:
- First person, direct
- Specific (exact line counts, benchmark numbers, actual errors)
- Skeptical of hype, enthusiastic about substance
- Never say "in conclusion" or "in summary" — just end when you're done

Audience: [WHO WILL READ THIS]
Length: [~1000 words for blog / ~500 for Twitter thread / ~300 for Gist]

Making Complex Research Accessible

For distilling papers or research into teachable content:

Distill this [paper/research/concept] into a teachable explainer.

Source: [PASTE ABSTRACT OR KEY SECTIONS]

Output:
1. ELI5 version: explain to a smart non-expert in 3 sentences
2. Key insight: what's the one thing this paper figured out?
3. The method in plain language: how did they do it? (no equations, just logic)
4. Why it matters: what does this enable that wasn't possible before?
5. The catch: what are the limitations or assumptions?
6. Nano-project idea: how could someone understand this by building a tiny version?

Teaching Code Review Checklist

When reviewing code with education-first lens:

Is this code teachable?
- [ ] Can a motivated beginner understand what it does in 5 minutes?
- [ ] Does each function have a comment explaining WHY, not just what?
- [ ] Are variable names descriptive enough to read like documentation?
- [ ] Is there a README that explains how to run it from scratch?
- [ ] Does it have at least one worked example in the comments?
- [ ] Are the non-obvious parts explained?

If any box is unchecked: the code isn't done yet.

Workflow

属于工作流:研究到发布(终点)+ 工作流:月度体检(终点)

位置上游下游
B的第4步karpathy-output-evolution(包装完成后)发布/分享
D的第4步karpathy-practice-environments(练习后)教程输出

研究到发布链路:autoresearch → llm-wiki → output-evolution → education-first 月度体检链路:meta-reflection → understanding-first → practice-environments → education-first

Prompt Contract

Convert <PROJECT_OR_CONCEPT> into a teaching version for beginners. Produce: 1) Core concept in one sentence, 2) Minimal runnable example (< 300 lines, no hidden deps), 3) Step-by-step walkthrough (explain WHY not just WHAT), 4) 3-5 progressive exercises (easy→hard), 5) Common misconceptions and how to check if you fell into them.

Verification Checklist

  • 核心概念用一句话能说清
  • 最小可运行示例确实能跑(< 300 行,无隐藏依赖)
  • walkthrough 解释了 WHY,不只是 WHAT
  • 练习题有递进难度
  • 常见误区有自检方法
  • 零基础读者能在 5 分钟内理解第一步

FAQ

What does the karpathy-education-first skill do?

Apply the education-first mindset — make everything you build teachable, create nano-project explanations, write for beginners. Use this skill when the user wants to explain a project to beginners, create a tutorial from code, write documentation that teaches (not just documents), make a concept accessible, or says "make this teachable", "explain like im a beginner", "nano project version", "teaching version", "explain from scratch", "blog post about this". Based on Karpathy 243-line GPT and Eureka Labs posts.

How do I install karpathy-education-first?

Run npx skills add https://github.com/learnprompt/andrej-karpathy-skills --skill karpathy-education-first in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/karpathy-education-first/ (personal) or .claude/skills/karpathy-education-first/ (project).

Which AI agents can use karpathy-education-first?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is karpathy-education-first safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains karpathy-education-first?

It is published by learnprompt in learnprompt/andrej-karpathy-skills. It has 127 recorded installs.

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