Add Karpathy Llm Wiki logo

Add Karpathy Llm Wiki

OrganizationPopular
nanocoai
add-karpathy-llm-wiki

Add a persistent wiki knowledge base to a NanoClaw group. Based on Karpathy's LLM Wiki pattern. Triggers on "add wiki", "wiki", "knowledge base", "llm wiki", "karpathy wiki".

Overview

Publishernanocoai
Repositorynanoclaw
Skill nameadd-karpathy-llm-wiki
Stars
30.9K
Forks
12.8K
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Add 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/nanocoai/nanoclaw/tree/main/.claude/skills/add-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/nanocoai/nanoclaw.git /tmp/nanoclaw
mkdir -p .claude/skills
cp -r /tmp/nanoclaw/.claude/skills/add-karpathy-llm-wiki .claude/skills/add-karpathy-llm-wiki
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add 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 Add 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 Add 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.

Add Karpathy LLM Wiki

Set up a persistent wiki knowledge base on NanoClaw, based on Karpathy's LLM Wiki pattern.

Each step is safe to re-run: directory creation uses mkdir -p, initial wiki files are created only if absent, the container skill is preserved unless the user opts to update it, and the group CLAUDE.md section is replaced in place via marker comments rather than duplicated.

Step 1: Read the pattern

Read ${CLAUDE_SKILL_DIR}/llm-wiki.md — this is the full LLM Wiki idea as written by Karpathy. Understand it thoroughly before proceeding. Summarize the core idea to the user briefly, then discuss what they want to build.

Step 2: Choose a group

AskUserQuestion: "Which group should have the wiki?"

  1. Main group — add to your existing main chat
  2. Dedicated group — create a new group just for the wiki
  3. Other — pick an existing group

If dedicated: ask which channel and chat, then register with pnpm exec tsx setup/index.ts --step register.

Step 3: Design collaboratively

Discuss with the user based on the pattern:

  • What's the wiki's domain or topic?
  • What kinds of sources will they add? (URLs, PDFs, images, voice notes, books, transcripts)
  • Do they want the full three-layer architecture or a lighter version?
  • Any specific conventions they care about? (The pattern intentionally leaves this open.)

Based on this discussion, create three things:

3a. Directory structure

Create wiki/ and sources/ directories in the group folder (mkdir -p — safe if they already exist). Create initial index.md and log.md per the pattern's Indexing and Logging section, adapted to the user's domain. Skip any of these files that already exist so a populated wiki is never clobbered on re-run.

3b. Container skill

Create container/skills/wiki/SKILL.md tailored to this user's wiki. This is the schema layer from the pattern — it tells the agent how to maintain the wiki. Base it on the pattern's Operations section (ingest, query, lint) and the conventions you agreed on with the user. Don't over-prescribe — the pattern says "your LLM figures out the rest."

If container/skills/wiki/SKILL.md already exists, ask the user whether to update it before overwriting, so an existing tailored schema is preserved on re-run.

3c. Group CLAUDE.md

Edit the group's CLAUDE.md to add a wiki section, wrapped in marker comments so it can be located and replaced on re-run:

markdown
<!-- BEGIN karpathy-llm-wiki -->
## Wiki
...section body...
<!-- END karpathy-llm-wiki -->

If a <!-- BEGIN karpathy-llm-wiki --> block already exists, replace it in place rather than appending a second copy. This is critical — it's what turns the agent into a wiki maintainer. The section should:

  • Explain the wiki system concisely: what it is, the three layers (sources, wiki, schema), the three operations (ingest, query, lint)
  • Index the key files and folders (wiki/, sources/, wiki/index.md, wiki/log.md)
  • Point to the container skill for detailed workflow
  • Ingest discipline: Be very explicit that when the user provides multiple files or points at a folder with many files, the agent MUST process them one at a time. For each file: read it, discuss takeaways, create/update all wiki pages (summary, entities, concepts, cross-references, index, log), and completely finish with that file before moving to the next. Never batch-read all files and then process them together — this produces shallow, generic pages instead of the deep integration the pattern requires.

Step 4: Source handling capabilities

Based on the source types the user plans to ingest (discussed in Step 3), check whether the agent can already handle those formats — some are supported natively, others need a skill (e.g. /add-image-vision, /add-pdf-reader, /add-voice-transcription). If a needed capability isn't installed, check if there's an available skill for it and help the user get it set up.

URL handling note

claude has built-in WebFetch, but it returns a summary, not the full document. For wiki ingestion of a URL where the full text matters, the container skill and CLAUDE.md should instruct claude to use bash commands to download full files instead. For example:

bash
curl -sLo sources/filename.pdf "<url>"

If the document is a webpage, then claude can use fetch or agent-browser to open the page and extract full text if available. The container skill and CLAUDE.md should note this so claude gets full content for sources rather than summaries.

Step 5: Optional lint schedule

AskUserQuestion: "Want periodic wiki health checks?"

  1. Weekly
  2. Monthly
  3. Skip — lint manually

If yes, ask the agent to schedule the lint task using the schedule_task MCP tool in conversation.

Step 6: Restart

Run from your NanoClaw project root:

bash
source setup/lib/install-slug.sh
launchctl kickstart -k gui/$(id -u)/$(launchd_label)  # macOS
systemctl --user restart $(systemd_unit)              # Linux

Tell the user to test by sending a source to the wiki group.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Add Karpathy Llm Wiki AI skill do?

Add a persistent wiki knowledge base to a NanoClaw group. Based on Karpathy's LLM Wiki pattern. Triggers on "add wiki", "wiki", "knowledge base", "llm wiki", "karpathy wiki".

Why use Add Karpathy Llm Wiki on TypingMind?

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

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

Which AI models can use Add 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 Add Karpathy Llm Wiki?

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

Is the Add Karpathy Llm Wiki AI skill free?

Yes. It is published on GitHub by nanocoai 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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