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/ingestnpx skills add skyllwt/AutoSci --skill ingestgit 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/ingest)<a href="https://agentmods.dev/skills/skyllwt/autosci/ingest"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ingest.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 5.1 | $0.00069 | $0.05345 |
| Opus 5 | $0.00034 | $0.02672 |
| Sonnet 5 | $0.00014 | $0.01069 |
| Haiku 4.5 | $0.00007 | $0.00534 |
Grade A, and why
ingest 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 6d 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ingest
Turn one paper into a fully wired set of wiki pages. Emit well-formed entities and correct cross-references; leave semantic audits (backlink symmetry, dangling nodes, field-value policing) for /check.
Use these local references on demand:
references/pdf-preprocessing.md— arXiv-ID recovery, tex fetching, prepare-paper handoff for direct PDF dropsreferences/dedup-policy.md— merge-vs-create decision rule for concepts and methods, and the line that separates/ingestshape checks from/checksemantic auditsreferences/cross-references.md— forward/reverse link matrix and paper-to-paper edge-type selectionreferences/init-mode.md— manifest-driven handoff from/initand parallel-safety conventionsreferences/error-handling.md— source parse, API, and slug-collision fallbacks
Open runtime/schema/entities.yaml for frontmatter field definitions and runtime/templates/{kind}.md.tmpl for body section structure. For index.md, log.md, and graph/ shapes, see runtime/schema/conventions.yaml and runtime/schema/edges.yaml.
Inputs
source: one of — arXiv URL (e.g.https://arxiv.org/abs/2106.09685), local.tex, local.pdf, or acanonical_ingest_pathhanded off by/initvia.checkpoints/init-sources.json(seereferences/init-mode.md)--discover(optional, default off): after the final report, invoke/discover --anchor <this-paper's-arxiv-id>and append the shortlist to the report as "Related papers you may want to ingest next". Never auto-ingests the suggestions. Skipped automatically in INIT MODE. Treat this as a user-owned flag: do not set it based on repo state.--visualize(optional, default off): after Step 7 rebuild, regenerate Canvas visualization artifacts viatools/visualize.py generate-canvas. Skipped automatically in INIT MODE — the parent/inithandles visualization once at fan-in. Treat this as a user-owned flag: do not set it based on repo state. (The interactive web Graph view lives in the SPA atapp/modules/graph.js, served bytools/serve.py; it readswiki/graph/live and needs no per-ingest regeneration.)
What ships with it
5 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.
- 6d ago First seen · 300 lines · 69 tokens per session scan A 05308aaad7e0
ingest is a skill published in the GitHub repository skyllwt/AutoSci (1,660 stars, last pushed 6d ago), licensed MIT. It adds 69 tokens to every session and 5,345 once invoked, about $0.0003 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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