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 zerolve-io/acornonaut-mcp --skill acornonaut-studygit clone --depth 1 https://github.com/zerolve-io/acornonaut-mcpWrote 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/zerolve-io/acornonaut-mcp/acornonaut-study)<a href="https://agentmods.dev/skills/zerolve-io/acornonaut-mcp/acornonaut-study"><img src="https://agentmods.dev/badge/skills/zerolve-io/acornonaut-mcp/acornonaut-study/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/zerolve-io/acornonaut-mcp/acornonaut-study"><img src="https://agentmods.dev/badge/skills/zerolve-io/acornonaut-mcp/acornonaut-study.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00065 | $0.00603 |
| Opus 5.5 | $0.00026 | $0.00241 |
| Sonnet 5.5 | $0.00013 | $0.00121 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
acornonaut-study 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 7d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Studying with Acornonaut
Acornonaut turns YouTube videos into flashcards and schedules them with spaced repetition. All
data lives in the user's Acornonaut account and is reached through the acornonaut MCP server.
Make flashcards from a video
- Call
add_video_by_urlwith the YouTube URL. PassplaylistNameonly if the user named one. - Card generation runs in the background and usually takes one to two minutes. Tell the user it has started; don't claim cards exist yet.
- When the user comes back, find the video with
get_playlist(using theplaylistIdfrom step- and show its cards with
get_video_cards.
- and show its cards with
- If a video finishes with no cards,
regenerate_cardsretries generation. Only setdeleteExisting: truewhen the user asks to replace cards they already have.
Run a review session
- Call
get_due_cards(optionally with aplaylistId). If nothing is due, say so and stop. - Show one card at a time: the question first, "Card N of Total", and wait for the user's answer.
- Reveal the answer, then ask how well they knew it: Again (1), Hard (2), Good (3) or Easy (4). Let the user rate it. Don't pick the rating for them; suggest "Again" when they couldn't recall it, because honest ratings make the schedule work.
- Call
record_reviewwith the card'scardIdand the rating, then move to the next card. - At the end, summarise how many cards were reviewed and how many were rated Again.
Find, edit and organise
- Topic questions ("my cards about X"):
search_cards. - Library overview:
list_playlists,get_playlist,get_stats. - Fixing a card:
update_card; adding one:create_card. Confirm beforedelete_cardordelete_playlist, which are permanent (deleting a playlist removes its videos and cards).
Export
export_deck with a playlistId and format: apkg returns an Anki download link, while csv,
json and markdown return the content directly.
Limits
Free accounts have playlist and monthly video limits. If a tool reports a limit, explain it plainly and point to https://acornonaut.app/pricing; don't retry in a loop.
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.
- 7d ago First seen · 47 lines · 65 tokens per session scan A b4d5b97e68c2
acornonaut-study is a skill published in the GitHub repository zerolve-io/acornonaut-mcp (0 stars, last pushed 3d ago), licensed MIT. It adds 65 tokens to every session and 603 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-11.
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