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Karpathy Llm Wiki

Community
LearnPrompt
karpathy-llm-wiki

Build and maintain an LLM-powered personal knowledge base or wiki. Use this skill when the user wants to organize research notes, build a second brain, set up an Obsidian vault with LLM compilation, ingest papers or articles automatically, or mentions "personal knowledge base", "LLM wiki", "second brain", "knowledge management", "ingest papers", "obsidian + LLM". Based on Karpathy Gist and 59k-like post.

Overview

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

Use it in TypingMind

Enable Karpathy Llm Wiki 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 Wiki 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 Wiki 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 2: LLM Wiki / Personal Knowledge Base(LLM知识库)

Source: https://x.com/karpathy/status/2039805659525644595 | https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f "59k likes — LLM Knowledge Bases" — top-2 most-liked post

Core Principle

You explore. The LLM maintains.

You read the world, capture raw signals. The LLM compiles, cross-links, deduplicates, lint-checks, and surfaces contradictions. Your Obsidian vault is the output, not the input.

Architecture

RAW INPUTS                 LLM LAYER                  OUTPUT
──────────                 ─────────                  ──────
Papers (PDF/arXiv)  ──→   ingest.py              ──→  entities/*.md
Articles (URL)      ──→   compile_wiki.py        ──→  index.md
Chat logs           ──→   lint_wiki.py           ──→  contradiction_log.md
Notes (voice/text)  ──→   query_wiki.py          ──→  slides/
                          update_entity.py       ──→  weekly_digest.md

The 5 Core Prompts

1. Ingest Prompt

You are a knowledge compiler. Given this raw source:

[PASTE RAW CONTENT]

Source URL/title: [SOURCE]
Date: [DATE]

Extract and output:
1. Key entities (people, concepts, tools, papers) — one per line with a 2-sentence description
2. Key claims with confidence level (high/medium/speculative)
3. Connections to existing topics: [LIST YOUR KNOWN TOPICS]
4. Contradictions with anything you know: [WHAT YOU ALREADY BELIEVE]
5. Suggested entity page updates (format: ENTITY_NAME | UPDATE_TEXT)

2. Compile Wiki Entry Prompt

You are maintaining a personal knowledge wiki. Update the entity page for [ENTITY_NAME].

Existing page content:
[CURRENT_PAGE_CONTENT]

New information to integrate:
[NEW_INFO]

Rules:
- Preserve all existing information; only add or correct
- Flag contradictions with: ⚠️ CONFLICT: [old claim] vs [new claim]
- Keep the page under 500 words
- End with: "Last updated: [DATE] | Sources: [LINKS]"

3. Weekly Ingest Prompt (run every week)

Process this week's reading list:
[PASTE 5-10 ITEMS: title + URL + 1-sentence summary]

For each item:
1. Extract top 3 entities to update
2. List 2-3 key claims worth adding to wiki
3. Note any contradictions with existing knowledge
4. Suggest connections to other entities in my wiki

Output as structured markdown, one section per item.

4. Lint / Contradiction Check Prompt

Review these wiki entries for consistency:

[PASTE 3-5 RELATED ENTITY PAGES]

Find and report:
1. Factual contradictions between pages
2. Duplicate information that should be merged
3. Outdated claims that should be flagged
4. Missing cross-links between related entities

Format: ISSUE | PAGE | LINE | SUGGESTED FIX

5. Query Prompt

Search my personal knowledge wiki to answer this question:

Question: [QUESTION]

Wiki context:
[PASTE RELEVANT PAGES]

Answer with:
1. Direct answer (2-3 sentences)
2. Supporting evidence from wiki (with page citations)
3. Gaps: what I don't know yet
4. Suggested reading to fill gaps

Memory / Context Hygiene (Anti-Pollution Rules)

Karpathy noted LLM memory "trying too hard" — a question from 2 months ago poisons every future conversation. Prevent this:

  • Never rely on LLM built-in memory for knowledge — use your wiki instead
  • Start fresh sessions for new topics; don't let old context bleed in
  • Explicit resets: If continuing a long session, open with "Ignore all prior context about X"
  • Version your wiki: Commit wiki changes with git so you can diff what changed

Folder Structure (Obsidian-Compatible)

wiki/
├── entities/
│   ├── people/
│   │   └── andrej-karpathy.md
│   ├── concepts/
│   │   ├── agentic-engineering.md
│   │   └── llm-simulator.md
│   └── tools/
│       └── claude.md
├── index.md              ← master entity list, auto-updated
├── contradiction_log.md  ← conflicts to resolve
├── weekly_digest/
│   └── 2026-W21.md
└── raw/                  ← source dumps before compilation

Weekly Cadence

Monday:    Dump week's reads into raw/
Tuesday:   Run ingest prompt on each item
Wednesday: Run lint/contradiction check
Thursday:  Update entity pages
Friday:    Generate weekly_digest.md with LLM

Workflow

属于工作流:研究到发布(内容创作者/研究者)

位置上游下游
第2步karpathy-autoresearch(研究产出原始素材)karpathy-output-evolution(将知识转化为可发布形式)

完整链路:autoresearch → llm-wiki → output-evolution → education-first

也在月度体检工作流中用于持久化反思结论。

Prompt Contract

text
Maintain this as an LLM wiki. From the new sources provided, update existing Markdown pages, create only necessary new pages, preserve all citations and source links, flag any contradictions with existing knowledge, and output a structured change log showing: pages updated, new pages created, contradictions found, and open questions.

Verification Checklist

  • 原始来源(raw sources)保持不可变
  • 每个 wiki 页面有来源链接
  • 矛盾之处被明确标记(不是静默覆盖)
  • changelog 记录了本次更新的所有变更
  • 没有创建不必要的新页面(优先更新已有页面)
  • 可以从 index 页面导航到任何实体

Frequently asked questions

What does the Karpathy Llm Wiki AI skill do?

Build and maintain an LLM-powered personal knowledge base or wiki. Use this skill when the user wants to organize research notes, build a second brain, set up an Obsidian vault with LLM compilation, ingest papers or articles automatically, or mentions "personal knowledge base", "LLM wiki", "second brain", "knowledge management", "ingest papers", "obsidian + LLM". Based on Karpathy Gist and 59k-like post.

Why use Karpathy Llm Wiki on TypingMind?

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

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

Which AI models can use Karpathy Llm Wiki?

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 Wiki?

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

Is the Karpathy Llm Wiki 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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