AutoSci is an AI research platform organized around a wiki, with an agent that supports stages of scientific work such as reading, experimentation, writing, and retaining knowledge across projects. It is for people building or using AI-assisted research workflows, with Claude Code, Codex, and OpenCode adaptations available. The catalogue add-ons extend those agent-specific workflows.
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/skyllwt/autosci/discovernpx skills add skyllwt/AutoSci --skill discovergit clone --depth 1 https://github.com/skyllwt/AutoSciWrote 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/skyllwt/autosci/discover)<a href="https://agentmods.dev/skills/skyllwt/autosci/discover"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/discover.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 | $0.00091 | $0.02998 |
| Opus 5 | $0.00046 | $0.01499 |
| Sonnet 5 | $0.00018 | $0.00600 |
| Haiku 4.5 | $0.00009 | $0.00300 |
Grade A, and why
discover 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/discover
Produce a ranked shortlist of paper candidates from one of four seed modes. Surface them to the user (or to the calling skill) with rationales. Never auto-ingest —
/discoveris a proposal stage,/ingestis the action stage.
Use these local references on demand:
references/seed-modes.md— when to pick anchor / topic / wiki / venue mode and how to translate the user's phrasing into onereferences/ranking-signals.md— whattools/discover.pyscores on and why discovery does not share/init's survey preferencereferences/wiki-dedup.md— how candidates are filtered againstwiki/papers/and what to do with matches
Inputs
--anchor <id>(repeatable): one or more anchor paper IDs (arXiv IDs preferred; S2 paperIds also accepted). Drives the anchor mode — the primary use case, including the post-/ingest"what to read next" flow.--negative <id>(repeatable, optional): IDs to push recommendations away from. Only meaningful with--anchor.--topic "<str>": a topic / query string. Drives the topic mode — lighter alternative to/init's planner.--from-wiki: derive seeds automatically from the wiki's most recently modified papers. Drives the wiki mode.--venue <slug>+--year <int>: venue slug and year (e.g.neurips2024). Drives the venue mode — ranks papers from that venue/year by relevance to the existing wiki.--limit N(optional, default 10): max shortlist size.
Exactly one of --anchor, --topic, --from-wiki, or --venue must be given.
Outputs
.checkpoints/discover-{seed-slug}-{YYYY-MM-DD}.json— full shortlist payload, machine-readable; the seed slug is derived from the first anchor or the topic- a human-readable markdown summary printed to the user with rationale per candidate
wiki/log.md— one append line viatools/research_wiki.py logfor anchor/topic/wiki runs only
/discover does not write anywhere else in wiki/ and does not touch raw/. from-venue is stricter: it does not write to wiki/ at all, including wiki/log.md. Whether to actually pull a candidate into the wiki is the caller's decision (a follow-up /ingest).
What ships with it
3 files 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 · 180 lines · 91 tokens per session scan A c4468afd7289
discover is a skill published in the GitHub repository skyllwt/AutoSci (1,660 stars, last pushed 6d ago), licensed MIT. It adds 91 tokens to every session and 2,998 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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