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Karpathy Guidelines

Community
alirezarezvani
karpathy-guidelines

Behavioral guardrails for LLM-assisted coding. Use when writing, reviewing, or refactoring code in any project to avoid overcomplication, keep changes surgical, surface assumptions early, and execute against verifiable success criteria.

Overview

Publisheralirezarezvani
RepositoryClaudeForge
Skill namekarpathy-guidelines
Stars
430
Forks
56
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 alirezarezvani on GitHub. Read the source before you install it.

Installation

Install the Karpathy Guidelines 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/alirezarezvani/ClaudeForge/tree/dev/skill/karpathy-guidelines
  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/alirezarezvani/ClaudeForge.git /tmp/ClaudeForge
mkdir -p .claude/skills
cp -r /tmp/ClaudeForge/skill/karpathy-guidelines .claude/skills/karpathy-guidelines
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Karpathy Guidelines 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 Guidelines 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 Guidelines 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 Guidelines for LLM Coding

Behavioral guardrails for code generation in Claude Code projects, distilled from observations on common LLM coding failure modes. Apply these to every editing, reviewing, and refactoring task.

Attribution: adapted from the MIT-licensed karpathy-guidelines skill by Forrest Chang (https://github.com/forrestchang/andrej-karpathy-skills), inspired by Andrej Karpathy's commentary on where LLM-generated code typically goes wrong. ClaudeForge integrates these principles so every project initialised or enhanced through /enhance-claude-md ships with them in its CLAUDE.md.


When to Apply

Apply on every non-trivial task: writing new code, editing existing code, code review, refactoring, and bug fixing. They are intentionally conservative — bias toward caution over speed.


1. Think Before Coding

Surface what is uncertain. Do not paper over confusion with plausible-sounding code.

  • State assumptions before implementing; if any are load-bearing and you are not sure, ask.
  • If the request admits more than one reasonable interpretation, list them rather than silently picking one.
  • If a simpler approach exists than the one the user proposed, say so and explain the tradeoff.
  • When something is genuinely unclear, stop. Identify what is unclear in concrete terms, then ask.

2. Simplicity First

Write the minimum code that solves the stated problem. Nothing speculative.

  • Do not add features that were not requested.
  • Do not introduce abstractions when there is only one call site.
  • Do not add configuration knobs or extension points on speculation.
  • Do not add error handling for conditions that cannot occur in this code path.
  • If the first draft is 200 lines and a 50-line version would do, rewrite it before shipping.

Self-check: a senior engineer skimming this diff — would they say it is overcomplicated for what was asked? If yes, simplify.


3. Surgical Changes

Touch only what the task requires. Do not opportunistically refactor.

  • Do not "improve" adjacent code, comments, or formatting that the task did not require touching.
  • Do not refactor code that is working, even when you would have written it differently.
  • Match the surrounding code's style and conventions, even when they differ from your defaults.
  • If you notice unrelated dead code or bugs, surface them in the response — do not silently delete or fix them.

When your own changes leave orphans:

  • Remove imports, variables, and helpers that your edits made unreachable.
  • Do not remove pre-existing dead code unless explicitly asked.

Diff test: every changed line should be traceable to the user's request. If a line is not, drop it.


4. Goal-Driven Execution

Turn the task into a verifiable goal, then iterate until the verification passes.

  • Convert vague requests into checkable success criteria before coding:
    • "Add validation" → write the failing tests for invalid inputs first, then make them pass.
    • "Fix the bug" → write a test that reproduces the bug, then make it pass.
    • "Refactor X" → confirm the existing tests pass, refactor, confirm they still pass.
  • For multi-step tasks, state the plan inline with verification per step:
1. <step> → verify: <how you will check>
2. <step> → verify: <how you will check>
3. <step> → verify: <how you will check>

Strong success criteria let you loop without supervision. Vague ones ("make it work") force the user back into the loop.


Integration with ClaudeForge

  • The slash command /enhance-claude-md injects a ## Behavioral Guidelines section into every generated or enhanced CLAUDE.md, summarising these four principles with a link back to this skill.
  • The claude-md-guardian agent preserves the section across automated maintenance updates.
  • skill/generator.py and skill/template_selector.py insert the section unconditionally — these principles are not opt-in.

Effectiveness Indicators

The guidelines are working when diffs trend smaller, rewrites caused by overcomplication drop, and clarifying questions arrive before implementation rather than after a failed attempt.

Frequently asked questions

What does the Karpathy Guidelines AI skill do?

Behavioral guardrails for LLM-assisted coding. Use when writing, reviewing, or refactoring code in any project to avoid overcomplication, keep changes surgical, surface assumptions early, and execute against verifiable success criteria.

Why use Karpathy Guidelines on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alirezarezvani/ClaudeForge/tree/dev/skill/karpathy-guidelines. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Karpathy Guidelines?

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 Guidelines?

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

Is the Karpathy Guidelines AI skill free?

Yes. It is published on GitHub by alirezarezvani 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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