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 agentmods add skills/gaasher/agent-loop-skills/karpathynpx skills add gaasher/Agent-Loop-Skills --skill karpathygit clone --depth 1 https://github.com/gaasher/Agent-Loop-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/gaasher/agent-loop-skills/karpathy)<a href="https://agentmods.dev/skills/gaasher/agent-loop-skills/karpathy"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/karpathy.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00132 | $0.02611 |
| Opus 5 | $0.00066 | $0.01306 |
| Sonnet 5 | $0.00026 | $0.00522 |
| Haiku 4.5 | $0.00013 | $0.00261 |
Grade A, and why
karpathy 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 5d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Karpathy Autoresearch
This is an experiment to have the LLM do its own research. You are a completely autonomous researcher:
you hack the training code with an idea, run it, keep the change if the metric improves and revert it if
it doesn't, advancing a branch as you go — and you repeat forever, until the human interrupts you.
The artifact is the <editable_files>; the feedback signal is one scalar <metric> (lower is better,
e.g. val_bpb) read from the run. Training runs in the user's own environment via <run_cmd> — this
skill installs nothing and imports nothing; it edits code, shells out, and reads the metric from the log.
When to use
Use this to leave an agent running on a single training script, optimizing one scalar metric hands-off,
where any improvement is kept and the loop never stops on its own. Default to broad freedom inside
<editable_files>; the only hard limit is that the run finishes within the budget without crashing. Not
for the analysis-first variant that reasons about the data before each edit (that is ml-autoresearch).
Setup
Resolve bindings interactively (load loop.run.yaml and skip if it already exists; else, on Claude Code
infer + recommend each via AskUserQuestion, otherwise ask as quoted prompts; write loop.run.yaml).
Then work with the user to set up a fresh run:
- Choose
<iter_strategy>— branches (one git commit per run; the original) or snapshots (one folder per run under<sandbox_root>/). Snapshots are safer on a dirty or gitignored tree; branches mirror Karpathy. Either is fully supported throughout the loop. - Open the run — branches: agree on a run tag from today's date (e.g.
mar5) and create the branchgit checkout -b autoresearch/<tag>(it must not already exist; this is a fresh run). snapshots: no branch — each iteration gets its own<sandbox_root>/iter<N>/. - Read the in-scope files — the repo is small; read them for full context: the README, the
read-only harness that defines the metric (the
<metric>ground truth — do not modify), and the<editable_files>you will hack (model/optimizer/training loop). - Verify the env/data exists — confirm
<run_cmd>can run (data shards, tokenizer, deps present). If not, tell the human the one command to prepare it (e.g.uv run prepare.py). - Initialize
results.tsv— create it with just the header row; the baseline is recorded after the first run. Leave it untracked (never commit it). - Confirm and go — confirm the setup looks right, then kick off the experimentation.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 149 lines · 132 tokens per session scan A 3a0fad621df1
karpathy is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (163 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 2,611 once invoked, about $0.0007 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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