DeepScientist is a local research studio that manages the cycle from baseline experiments through research findings and paper-ready outputs. Researchers use it to organize autonomous scientific investigations, review progress, and take control when needed. The catalogue add-ons provide workflows and agent integrations for running research projects with it.
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 ResearAI/DeepScientist --skill alpharxiv-paper-loopup.rootbakgit clone --depth 1 https://github.com/ResearAI/DeepScientistWrote 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/researai/deepscientist/alpharxiv-paper-loopup.rootbak)<a href="https://agentmods.dev/skills/researai/deepscientist/alpharxiv-paper-loopup.rootbak"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/alpharxiv-paper-loopup.rootbak/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/researai/deepscientist/alpharxiv-paper-loopup.rootbak"><img src="https://agentmods.dev/badge/skills/researai/deepscientist/alpharxiv-paper-loopup.rootbak.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.00604 |
| Opus 5 | $0.00020 | $0.00302 |
| Sonnet 5 | $0.00008 | $0.00121 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
alphaxiv-paper-lookup scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://alphaxiv.org/overview/{PAPER_ID}.md" Copies of this mod
2 near-identical copies found in the catalogue:
- alphaxiv-paper-lookup — 100% identical, 7 lines differ
- alphaxiv-paper — 86% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaXiv Paper Lookup
Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. This is faster and more reliable than trying to read a raw PDF.
When to Use
- User shares an arxiv URL (e.g.
arxiv.org/abs/2401.12345) - User mentions a paper ID (e.g.
2401.12345) - User asks you to explain, summarize, or analyze a research paper
- User shares an alphaxiv URL (e.g.
alphaxiv.org/overview/2401.12345)
Workflow
Step 1: Extract the paper ID
Parse the paper ID from whatever the user provides:
| Input | Paper ID |
|---|---|
https://arxiv.org/abs/2401.12345 |
2401.12345 |
https://arxiv.org/pdf/2401.12345 |
2401.12345 |
https://alphaxiv.org/overview/2401.12345 |
2401.12345 |
2401.12345v2 |
2401.12345v2 |
2401.12345 |
2401.12345 |
Step 2: Fetch the machine-readable report
curl -s "https://alphaxiv.org/overview/{PAPER_ID}.md"
This returns the intermediate machine-readable report �� a structured, detailed analysis of the paper optimized for LLM consumption. One call, plain markdown, no JSON parsing.
If this returns 404, the report hasn't been generated for this paper yet.
Step 3: If you need more detail, fetch the full paper text
If the report doesn't contain the specific information the user is asking about (e.g. a particular equation, table, or section), fetch the full paper text:
curl -s "https://alphaxiv.org/abs/{PAPER_ID}.md"
This returns the full extracted text of the paper as markdown. Only use this as a fallback �� the report is usually sufficient.
If this returns 404, the full text hasn't been processed yet. As a last resort, direct the user to the PDF at https://arxiv.org/pdf/{PAPER_ID}.
Error Handling
- 404 on Step 2: Report not generated for this paper.
- 404 on Step 3: Full text not yet extracted for this paper.
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.
- 11d ago First seen · 60 lines · 40 tokens per session scan A ccef6ac5cb16
alphaxiv-paper-lookup is a skill published in the GitHub repository ResearAI/DeepScientist (3,323 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 604 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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