karpathy-system-prompt-learning

karpathy-system-prompt-learning is a skill for Claude Code from LearnPrompt/andrej-karpathy-skills. It costs 113 tokens per session (1,823 once invoked), scanned A, original, MIT.

A method for improving an AI model's behavior by writing the needed background, examples, and decision rules directly into its system prompt.

In plain words
What is it for?
Use it to design prompts that teach a model a task, explain its context, demonstrate correct handling, and specify rules for common errors.
Why use it?
It makes the agent's expected approach clearer and more consistent without retraining the model.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Part of the karpathy-skills plugin — 15 skills shipped together

Good fit Use it to design prompts that teach a model a task, explain its context, demonstrate correct handling, and specify rules for common errors.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning
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.

Any agent
npx skills add LearnPrompt/andrej-karpathy-skills --skill karpathy-system-prompt-learning
Clone the repo
git clone --depth 1 https://github.com/LearnPrompt/andrej-karpathy-skills

Made for: Claude Code.

Or install karpathy-skills, the plugin that ships this one along with the rest of its 15 skills.

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-system-prompt-learning

README.md
[![agentmods](https://agentmods.dev/badge/skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning/github.svg)](https://agentmods.dev/skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning)
Your own site
<a href="https://agentmods.dev/skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning"><img src="https://agentmods.dev/badge/skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for karpathy-system-prompt-learning

Your own site · 80×15
<a href="https://agentmods.dev/skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning"><img src="https://agentmods.dev/badge/skills/learnprompt/andrej-karpathy-skills/karpathy-system-prompt-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 113 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,823 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 126
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 5.1 $0.00113 $0.01823
Opus 5 $0.00056 $0.00911
Sonnet 5 $0.00023 $0.00365
Haiku 4.5 $0.00011 $0.00182

Measured 12d ago against content hash d46c8734cdcb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

karpathy-system-prompt-learning 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 12d ago.

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.

karpathy-system-prompt-learning/SKILL.md · 242 lines

How it starts

The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill 13: System Prompt Learning(系统提示学习范式)

Source: https://x.com/karpathy/status/1921368644069765486 "System prompt learning — missing LLM learning paradigm"

Core Principle

The system prompt is a textbook. Write it like one.

Karpathy's insight: there are three learning modes for LLMs (pretrain, finetune, RL) — but there's a fourth that's massively underused: system prompt learning. Explicitly encoding strategy into the system prompt is more sample-efficient than retraining and more reliable than hoping the model figures it out.

Think of it as writing a textbook for the LLM before each task. Not just instructions — strategy with reasoning.

The System Prompt Learning Framework

Good system prompts have three layers (mirroring how humans learn):

Layer 1: EXPOSITION (pretrain equivalent)
→ Background knowledge the model needs
→ Relevant concepts, terminology, context
→ "Here's what you need to know about [domain]"

Layer 2: WORKED EXAMPLES (SFT equivalent)  
→ Step-by-step demonstration of correct behavior
→ Explicit reasoning traces
→ "Here's exactly how to handle [situation]"

Layer 3: STRATEGY (RL equivalent)
→ Explicit decision rules
→ Error patterns to avoid
→ "When you see X, do Y because Z"

Building a System Prompt from Scratch

Build a system prompt that teaches an LLM to [TASK] reliably.

Task description: [WHAT THE LLM SHOULD DO]
Current failure mode: [HOW IT CURRENTLY GOES WRONG]
Desired behavior: [EXACTLY WHAT GOOD LOOKS LIKE]

Structure the system prompt with all three learning layers:

Layer 1 — Exposition:
Write 2-3 paragraphs of background knowledge the LLM needs. 
Include: key concepts, relevant context, domain vocabulary.

Layer 2 — Worked Examples:
Write 2 complete examples of the task done correctly.
Format: INPUT → [step-by-step reasoning] → OUTPUT
Show the thinking, not just the answer.

Layer 3 — Strategy:
Write explicit decision rules as IF/THEN/BECAUSE statements.
Include: common mistakes to avoid, edge cases to handle, quality checks.

End with: "Before responding, verify: [checklist of 3-5 quality checks]"

Read the full file on GitHub · 242 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. 12d ago First seen · 242 lines · 113 tokens per session scan A d46c8734cdcb

Subscribe to this mod's changes

karpathy-system-prompt-learning is a skill published in the GitHub repository LearnPrompt/andrej-karpathy-skills (97 stars, last pushed 2mo ago), licensed MIT. It adds 113 tokens to every session and 1,823 once invoked, about $0.0006 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 2026-08-30.

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