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.
npx skills add LearnPrompt/andrej-karpathy-skills --skill karpathy-autoresearchgit clone --depth 1 https://github.com/LearnPrompt/andrej-karpathy-skillsWrote 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.
[](https://agentmods.dev/skills/learnprompt/andrej-karpathy-skills/karpathy-autoresearch)<a href="https://agentmods.dev/skills/learnprompt/andrej-karpathy-skills/karpathy-autoresearch"><img src="https://agentmods.dev/badge/skills/learnprompt/andrej-karpathy-skills/karpathy-autoresearch/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.
<a href="https://agentmods.dev/skills/learnprompt/andrej-karpathy-skills/karpathy-autoresearch"><img src="https://agentmods.dev/badge/skills/learnprompt/andrej-karpathy-skills/karpathy-autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
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 128 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.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.01450 |
| Opus 5 | $0.00052 | $0.00725 |
| Sonnet 5 | $0.00021 | $0.00290 |
| Haiku 4.5 | $0.00010 | $0.00145 |
Grade A, and why
karpathy-autoresearch 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.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill 6: AutoResearch(自主研究循环)
Source: https://x.com/karpathy/status/2030371219518931079 "Autoresearch project — agent self-iterates training code" — 28k likes
Core Principle
You change the prompt. The agent changes everything else.
The loop: agent reads current state → runs experiment → analyzes results → proposes next iteration → you approve the prompt change → repeat.
You stay in the hypothesis space. Agent stays in the implementation + execution space.
The AutoResearch Architecture
Human Layer (you):
- Define research question
- Approve hypothesis changes
- Set evaluation metric
- Stop when satisfied
Agent Layer:
- Implement current hypothesis
- Run experiment (on git branch)
- Analyze logs/results
- Propose next hypothesis
- Never change the research question
Setup Prompt (One-Time)
You are AutoResearcher for this project.
Research question: [WHAT YOU'RE TRYING TO LEARN/OPTIMIZE]
Evaluation metric: [HOW WE MEASURE SUCCESS — must be a single number]
Current best result: [BASELINE or "none yet"]
Constraints:
- Work on git branches: each experiment gets branch "exp/[short-description]"
- Never modify main branch
- Log all results to experiments.jsonl in format: {"id": N, "hypothesis": "...", "metric": value, "notes": "..."}
- Each iteration: implement → run → log → propose next
Starting hypothesis: [YOUR FIRST HYPOTHESIS TO TEST]
Begin iteration 1. Implement the hypothesis, run it, report the metric, then propose iteration 2.
Per-Iteration Loop Prompt
AutoResearcher: Iteration [N]
Current state:
- Branch: exp/[current]
- Metric so far: [RESULTS LOG]
- Best result: [BEST SO FAR]
Completed last iteration: [WHAT HAPPENED]
Metric result: [VALUE]
Your tasks this iteration:
1. Analyze: what does the result tell us about the hypothesis?
2. Propose: what's the next hypothesis? (one variable change at a time)
3. Implement: write the code change for this hypothesis
4. Run: execute and report the metric
5. Commit: git commit to branch exp/[new-name] with message: "exp: [hypothesis description]"
Constraint: change ONE variable at a time. If last experiment failed, back off to simpler hypothesis.
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.
- 12d ago First seen · 158 lines · 104 tokens per session scan A 510e3799d782
karpathy-autoresearch is a skill published in the GitHub repository LearnPrompt/andrej-karpathy-skills (97 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 1,450 once invoked, about $0.0005 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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