Karpathy Wiki

Community

SherwinQ

karpathy-wiki

Use when building or maintaining a personal knowledge base with LLM assistance. Triggers on ingesting sources, compiling wiki articles, querying knowledge, running health checks, deep research, or initializing a new wiki vault. Based on Karpathy's LLM Wiki pattern with Obsidian integration.

Overview

Publisher	SherwinQ
Repository	`karpathy-wiki`
Skill name	`karpathy-wiki`
Stars	117
Forks	17
Bundled files	20
License	MIT
Links	Skill source Repository

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.
20 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by SherwinQ on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/SherwinQ/karpathy-wiki/tree/main
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.

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/SherwinQ/karpathy-wiki.git \
  .claude/skills/karpathy-wiki

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Karpathy 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 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 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.

Karpathy Wiki

LLM-driven personal knowledge base that compounds over time. Raw sources in, structured wiki out.

You never write the wiki. The LLM writes everything. You just steer, and every answer compounds.

When to Use

User wants to ingest documents, URLs, or text into a knowledge base
User wants to query, compile, or maintain wiki articles
User needs deep research to fill knowledge gaps
User wants to initialize a new wiki vault

When NOT to Use

One-off Q&A without persistent knowledge storage
Note-taking without cross-referencing or structure
Non-text content management (images, videos alone)

Core Principles

raw/ is immutable — sources are never edited; re-ingest as new version
purpose.md anchors direction — read on every operation, align output with wiki goals
Classify before extract — determine entity type during Ingest, choose chapter structure during Compile
Analyze before generate — two-step Compile: structured analysis → article generation
Backlink audit is mandatory — bidirectional links are the knowledge graph
Query answers must be archived — good answers are compounding fuel
Token budget first — always read index before full articles (L0→L1→L2→L3)
Human verifies, LLM maintains — LLM writes, user steers

Pipeline

Stage	Input	Output
Ingest	URLs, files, text	`raw/` (immutable) + hash cache
Compile	raw/ files	`wiki/<domain>/` articles by entity type
Query	Questions	Cited answers → `outputs/queries/`
Lint	Full wiki	Health report → `outputs/reports/`

Each stage appends log.md. Lint and Query can trigger Deep Research to fill gaps.

Token Budget

Level	~Tokens	When	What
L0	~200	Every session	SKILL.md frontmatter
L1	~1-2K	Session start	purpose.md + Concept Index + Dashboard
L2	~2-5K	Search	Result summaries
L3	5-20K	Deep read	Full articles or raw sources

Entity Types

7 types, each with different required sections (see references/entity-types.md):

concept (default) · person · tool · event · comparison · pattern · overview

Commands

Command	Action
`init [path] [--template <t>]`	Initialize vault (templates: general/research/reading/project)
`ingest <source>`	Collect + classify + archive (no wiki generation)
`compile [topic]`	Two-step: analyze → generate wiki article
`query <question>`	Answer from wiki, archive result
`promote`	Elevate quality query answer to wiki article
`research <topic>`	Gap → web search → ingest → compile loop
`delete <source>`	Safe delete with cascade cleanup
`lint`	Health check + graph analysis

Conventions

Obsidian CLI first for page operations (see references/obsidian-cli-cheatsheet.md)
Structured diff for updates: Current/Proposed/Reason/Source, user confirms each
Sources traceability: frontmatter sources[] links every wiki page to raw/ files
Incremental cache: SHA256 hash in .cache/ skips unchanged sources

References

references/operations.md — Full procedures for all 9 operations
references/frontmatter-schemas.md · compilation-guide.md · entity-types.md · obsidian-cli-cheatsheet.md
assets/ — All templates (purpose, wiki article, raw, query, indexes, log)
scripts/ — init-wiki.sh (with --template), parse-media.py

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 Karpathy Wiki AI skill do?

Why use Karpathy Wiki on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SherwinQ/karpathy-wiki/tree/main. 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 Karpathy 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 Wiki?

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

Is the Karpathy Wiki AI skill free?

Yes. It is published on GitHub by SherwinQ 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Karpathy Wiki

When to Use

When NOT to Use

Core Principles

Pipeline

Token Budget

Entity Types

Commands

Conventions

References

Bundled files

Frequently asked questions

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