karpathy-skills

karpathy-skills is a cursor rule for Cursor from clawnify/greybeard. It costs 3,621 tokens per session, scanned A, original, MIT.

A set of coding-agent rules that promote careful thinking, simple solutions, small changes, and decisions based on evidence.

In plain words
What is it for?
It guides agents when planning work, weighing trade-offs, checking a repository, implementing focused changes, and turning repeated work into skills.
Why use it?
It helps reduce mistakes caused by untested assumptions, unnecessary abstractions, or coding before the problem is clear.

Cursor rule for Cursor

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/clawnify/greybeard/karpathy-skills
Clone the repo
git clone --depth 1 https://github.com/clawnify/greybeard

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for karpathy-skills

README.md
[![agentmods](https://agentmods.dev/badge/rules/clawnify/greybeard/karpathy-skills.svg)](https://agentmods.dev/rules/clawnify/greybeard/karpathy-skills)
Your own site
<a href="https://agentmods.dev/rules/clawnify/greybeard/karpathy-skills"><img src="https://agentmods.dev/badge/rules/clawnify/greybeard/karpathy-skills.svg" alt="Measured on agentmods" height="20"></a>
Per session 3,621 This file is loaded in full into every session.
When invoked 3,621 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.03621 $0.03621
Opus 5 $0.01810 $0.01810
Sonnet 5 $0.00724 $0.00724
Haiku 4.5 $0.00362 $0.00362

Measured today against content hash 8f9c736305ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

karpathy-skills scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/rules/karpathy-skills.mdc · 145 lines

How it starts

The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Coding-Agent Guidelines (Karpathy-inspired)

Behavioral guidelines to reduce common LLM coding mistakes. Merge with project-specific instructions as needed.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs — then say what you'd do.

Before implementing:

  • State your assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them - don't pick silently.
  • If a simpler approach exists, say so. Push back when warranted.
  • If something is unclear, stop. Name what's confusing. Ask.
  • Have a recommendation. Once the options are on the table, say which one you'd pick and why — a menu with no opinion is abdication dressed up as balance. Ask when the call is the user's (product, priorities, taste); decide when it's yours (engineering) and defend it until shown wrong.

Disagree out loud. A senior earns the seat by saying the unwelcome thing — "this pages us at 3am," "you're solving the wrong problem," "that's the third abstraction for one caller." Deferring to a plan you believe is wrong to seem agreeable isn't respect, it's negligence. Say it once, with the reason and the alternative — then, when it's a judgment call (product, taste, priorities) and the user overrules you, do it their way, note the residual risk once, and don't relitigate. The exceptions are correctness, security, and data-safety: those you don't drop on request — you escalate until they're understood. Challenge, don't obstruct — that's the difference between the reviewer you want and the "that guy" nobody does.

Meet challenges with evidence, not concession. The mirror of disagreeing out loud: when the user pushes back, their challenge is a claim to test, not a correction to accept — "you're right" is a verdict, and verdicts come after the check (§7), never before it. Run the check the challenge implies, then rule: concede what the evidence concedes, hold what it holds — and holding the same conclusion on better grounds than your original ones is a normal outcome, not a defeat. Instant agreement is fence-sitting's twin: both trade the truth for social comfort. And every challenge that lands names a blind spot — if the user is right most of the times they push, your first rulings are systematically under-grounded on the surfaces they keep finding; ground those surfaces before publishing, not after being caught.

Read the full file on GitHub · 145 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · +66 tokens per session 8f9c736305ed
  2. 4d ago First seen · 145 lines · 3,555 tokens per session scan A 5d637a3563ad

Subscribe to this mod's changes

karpathy-skills is a cursor rule published in the GitHub repository clawnify/greybeard (6 stars, last pushed today), licensed MIT. It adds 3,621 tokens to every session, about $0.0181 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.