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Karpathy Llm Simulator

Community
LearnPrompt
karpathy-llm-simulator

Use LLM as a simulator of expert debates and opposing viewpoints instead of getting a single sycophantic answer. Use this skill when the user needs to make an important decision, wants to stress-test an idea, needs devil's advocate analysis, wants to avoid confirmation bias, or says "argue against this", "what are the counterarguments", "simulate experts", "debate this", "challenge my thinking". Based on Karpathy 31k-like post.

Overview

PublisherLearnPrompt
Repositoryandrej-karpathy-skills
Skill namekarpathy-llm-simulator
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 Llm Simulator 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-llm-simulator
  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-llm-simulator .claude/skills/karpathy-llm-simulator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Karpathy Llm Simulator 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 Llm Simulator 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 Llm Simulator 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 3: LLM as Simulator(LLM模拟器思维)

Source: https://x.com/karpathy/status/2037921699824607591 | https://x.com/karpathy/status/2049907410303865030 "Drafted a blog post → LLM argue the opposite" — 31k likes

Core Principle

Don't ask what the LLM thinks. Ask it to simulate what a diverse group of experts would argue.

LLMs are trained to please. A direct question gets a sycophantic answer. A simulation request gets a distribution of real perspectives — including the uncomfortable ones.

Karpathy's method: write a draft → ask LLM to argue the strongest possible opposite position → synthesize a better view.

The 4 Simulator Modes

Mode 1: Expert Debate Panel

Best for: technical decisions, architecture choices, research directions

Simulate a structured debate between these 3 expert personas on [TOPIC/DECISION]:

Expert A: [most optimistic / pro position]
Expert B: [most skeptical / con position]  
Expert C: [pragmatic outsider / unexpected angle]

For each expert:
- State their core argument in 3 sentences
- Cite 2 specific examples or data points they'd use
- Identify what they'd say is the FATAL FLAW in the opposing view

After the debate, synthesize: what's the strongest hybrid position that survives all three critiques?

Topic: [YOUR_TOPIC]
My current position: [YOUR_DRAFT_VIEW]

Mode 2: Steel Man the Opposite

Best for: before publishing, before committing to a decision

I'm about to [ACTION / PUBLISH / DECIDE]:

[YOUR PLAN OR DRAFT]

Steel man the strongest possible argument AGAINST this. Be merciless.
Don't hedge. Don't say "while this has merit...". 
Argue as if you genuinely believe the opposite and need to convince a skeptical expert.

Then: what would it take to make my original position survive this attack?

Mode 3: Pre-Mortem Simulation

Best for: project planning, product launches, major decisions

Imagine it's [DATE 6 MONTHS FROM NOW] and [YOUR PROJECT/PLAN] has failed completely.

Simulate 3 different failure modes — each from a different root cause:
1. Technical failure: what went wrong in the implementation?
2. Strategic failure: what assumption proved wrong?
3. Execution failure: what human/process error occurred?

For each: describe the specific sequence of events that led to failure.

Then: what early warning signals would have been visible by [DATE 1 MONTH FROM NOW]?

Mode 4: Socratic Drill

Best for: testing your own understanding of a concept

I believe I understand [CONCEPT]. 

Ask me 5 increasingly hard Socratic questions to test my understanding.
After each answer I give, probe a potential gap or inconsistency.
At the end, grade my understanding on a scale of 1-10 and identify my biggest blind spot.

Start with question 1.

Anti-Sycophancy Patterns

Always add one of these to prevent the LLM from capitulating to your framing:

  • "Do not soften your critique because you think I want to hear it."
  • "If my position is clearly wrong, say so directly."
  • "Ignore that I wrote the original draft — evaluate it as an anonymous submission."
  • "Your job is to find the fatal flaw, not to validate."

Decision Framework

Before any major decision:
1. Write down your current position (1-2 paragraphs)
2. Run Mode 2 (Steel Man the Opposite)
3. Update your position based on the strongest critique
4. Run Mode 1 (Expert Debate) to get broader perspective
5. Make the decision — now you've genuinely stress-tested it

Total time: 15-20 minutes. Quality of decision: dramatically better.

When NOT to Use This

  • For factual lookups (just ask directly)
  • For code generation (use agentic engineering instead)
  • When you've already decided and need to execute (move forward, don't ruminate)

Workflow

属于工作流:反偏见决策(任何重要判断)

位置上游下游
入口用户面临重要决策时直接触发karpathy-understanding-first(验证自己是否真正理解)

完整链路:llm-simulator → understanding-first → system-prompt-learning

也在月度体检工作流中用于 stress-test 月度结论。

Prompt Contract

text
Simulate a structured debate between 3-5 expert personas with conflicting incentives on <DECISION/TOPIC>. Each persona must: state their core argument, cite specific evidence, identify the fatal flaw in opposing views. After the debate, synthesize the strongest hybrid position that survives all critiques. End with: key assumptions I must verify myself.

Verification Checklist

  • 至少有 3 个角色,且利益/立场互相冲突
  • 每个角色都给出了具体论据(不是泛泛而谈)
  • 每个角色都指出了对方的致命缺陷
  • 综合结论经受住了所有角色的攻击
  • 列出了「我必须亲自验证」的假设清单
  • 没有出现讨好用户原始立场的倾向

Frequently asked questions

What does the Karpathy Llm Simulator AI skill do?

Use LLM as a simulator of expert debates and opposing viewpoints instead of getting a single sycophantic answer. Use this skill when the user needs to make an important decision, wants to stress-test an idea, needs devil's advocate analysis, wants to avoid confirmation bias, or says "argue against this", "what are the counterarguments", "simulate experts", "debate this", "challenge my thinking". Based on Karpathy 31k-like post.

Why use Karpathy Llm Simulator on TypingMind?

Because you install it once and use it with any model. Karpathy Llm Simulator 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 Llm Simulator in TypingMind?

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

Which AI models can use Karpathy Llm Simulator?

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 Llm Simulator?

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

Is the Karpathy Llm Simulator 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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