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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Create consistent nn_cfd W&B experiment comparison reports from selected run IDs using wandb and wandb_workspaces. Use this whenever the user asks to report experiment results, create a W&B Report, compare CFD training/eval runs, document baseline-vs-variant outcomes, reproduce the project experiment report template, or publish a leaderboard-style report for milieu/nn_cfd.
Search for research papers and academic content using Exa advanced search. Full filter support including date ranges and text filtering. Use when searching for academic papers, arXiv preprints, or scientific research.
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.
Search for research papers and academic content using Exa advanced search. Full filter support including date ranges and text filtering. Use when searching for academic papers, arXiv preprints, or scientific research.
Create or improve Senpai target-repository onboarding files: program.md plus instructions/prompt-advisor.md and instructions/prompt-student.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, advisor/student prompts, or prepare a repo for autonomous advisor/student experiment loops.
Check and respond to GitHub Issues from the human researcher team. Runs in a forked context (no access to main conversation). Use this skill whenever you need to: check for human messages, respond to human issues, poll for team communications, check GitHub issues. Also triggers for: "any human messages?", "check issues", "respond to humans".
| name | alphaxiv-paper-lookup |
| description | 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. |
| model | Codex-opus-4-8 |
| effort | high |
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.
arxiv.org/abs/2401.12345)2401.12345)alphaxiv.org/overview/2401.12345)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 |
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.
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}.