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Karpathy Agentic Engineering

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LearnPrompt
karpathy-agentic-engineering

Apply Karpathy-style agentic engineering to any coding or building task. Use this skill when the user wants to set up an agent-driven workflow, needs to write a CLAUDE.md or AGENTS.md, wants to orchestrate agents with clear success criteria, or says things like "agent loop", "let the agent handle it", "agentic coding", "set up agent workflow". Includes the 7-step inner loop from Karpathy claude coding notes.

Overview

PublisherLearnPrompt
Repositoryandrej-karpathy-skills
Skill namekarpathy-agentic-engineering
Stars
118
Forks
18
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by LearnPrompt on GitHub. Read the source before you install it.

Installation

Install the Karpathy Agentic Engineering AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LearnPrompt/andrej-karpathy-skills/tree/main/karpathy-agentic-engineering
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LearnPrompt/andrej-karpathy-skills.git /tmp/andrej-karpathy-skills
mkdir -p .claude/skills
cp -r /tmp/andrej-karpathy-skills/karpathy-agentic-engineering .claude/skills/karpathy-agentic-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Karpathy Agentic Engineering in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Karpathy Agentic Engineering on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Karpathy Agentic Engineering is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill 1: Agentic Engineering(代理工程)

Source: https://x.com/karpathy/status/2004607146781278521 | https://x.com/karpathy/status/2015883857489522876 | https://x.com/karpathy/status/2026731645169185220 "It is hard to communicate how much programming has changed." — ~37k likes

Core Principle

You are no longer writing code. You are directing a team of tireless junior engineers.

The shift: write code → write tasks with success criteria → agent loops → human taste review

The 7-Step Inner Loop (Karpathy's Incremental Coding Rhythm)

This is the micro-loop for every coding session:

  1. Stuff context — Load entire project into context (files-to-prompt, relevant docs, recent diffs)
  2. Discuss plan — Ask agent for high-level approaches with pros/cons before any code
  3. Request first draft — Pick one approach, get a minimal first implementation
  4. Inline learning — Manually review the diff; ask agent to explain any API you don't recognize
  5. Test — Run tests, check outputs, verify success criteria
  6. Commit — Only commit what you understand and have verified
  7. Ask for next — Let agent suggest the next incremental step

Never skip step 4. Understanding atrophies if you rubber-stamp every diff.

CLAUDE.md / AGENTS.md Template

Write this file at the project root to prevent the most common agent failure modes:

markdown
# Agent Instructions for [Project Name]

## Behavior Rules
- Make one incremental change at a time. Do NOT refactor unrelated code.
- Always explain WHY you're making a change before the diff.
- If unsure about intent, ASK before implementing.
- Do NOT add new dependencies without explicit approval.
- Prefer simple, readable solutions over clever ones.

## Success Criteria (task-specific — fill in per session)
- [ ] Tests pass: `[test command]`
- [ ] No new lint errors: `[lint command]`
- [ ] Behavior matches: [description of expected output]

## Project Context
- Stack: [language, framework, key libs]
- Entry point: [file]
- Test command: [command]
- Forbidden: [list things agent must never do]

Master Prompt Template

You are an agentic engineer on this project.

Project context: [paste relevant files or use files-to-prompt output]

Task: [specific, unambiguous description]

Success criteria (ALL must be true when done):
1. [verifiable check 1]
2. [verifiable check 2]
3. [verifiable check 3]

Constraints:
- Only change files relevant to this task
- No new dependencies without asking
- Output: one incremental diff at a time

Start by proposing a high-level plan with 2-3 options and trade-offs. Wait for approval before writing code.

Common Failure Modes to Prevent

Agent SinPrevention
Over-refactoring"Only touch files directly related to the task"
Sycophantic confirmation"Tell me what could go wrong with this plan"
Hallucinated APIs"Cite the exact line in the docs for any API you use"
Dependency creep"No new pip/npm installs without asking"
No success criteriaAlways define DONE before starting

Workflow

属于工作流:想法到上线(开发者)

位置上游下游
第2步karpathy-idea-files(先定义想法)karpathy-minimalism(极简实现)

完整链路:idea-files → agentic-engineering → minimalism → supply-chain-hygiene → vibe-to-agentic

也常在反偏见决策工作流中作为执行层出现——当决策完成后需要落地时,切换到本 Skill。

Prompt Contract

text
You are an agentic engineer. Project context: <files, constraints, current state>. Task: <specific goal>. Success criteria: <verifiable checklist>. First propose a high-level plan with tradeoffs. Then make only the next incremental change. After each change, run the relevant verification and report: changed files, evidence of success, remaining risk, and next step.

Verification Checklist

  • 成功标准已经写成可检查项目(不是模糊描述)
  • 每轮变更都有实际验证证据:测试通过、lint clean、diff 范围正确
  • Agent 没有修改任务范围外的文件
  • 人类已 review 每个 diff 中不理解的部分(不跳步)
  • 最终提交前,能用一句话向同事解释改了什么、为什么

When to Apply This Skill

  • Starting any coding task > 30 minutes
  • Setting up a new project with agent assistance
  • Debugging a complex issue (agent as rubber duck + implementer)
  • Writing CLAUDE.md / AGENTS.md for a repo

Frequently asked questions

What does the Karpathy Agentic Engineering AI skill do?

Apply Karpathy-style agentic engineering to any coding or building task. Use this skill when the user wants to set up an agent-driven workflow, needs to write a CLAUDE.md or AGENTS.md, wants to orchestrate agents with clear success criteria, or says things like "agent loop", "let the agent handle it", "agentic coding", "set up agent workflow". Includes the 7-step inner loop from Karpathy claude coding notes.

Why use Karpathy Agentic Engineering on TypingMind?

Because you install it once and use it with any model. Karpathy Agentic Engineering is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Karpathy Agentic Engineering in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LearnPrompt/andrej-karpathy-skills/tree/main/karpathy-agentic-engineering. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Karpathy Agentic Engineering?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Karpathy Agentic Engineering?

As many as you like. As long as a model supports skills, you can use Karpathy Agentic Engineering with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Karpathy Agentic Engineering AI skill free?

Yes. It is published on GitHub by LearnPrompt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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