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auto-paper-demo自动纸张演示

Agent Skill

auto-paper-demo 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

9,359

周安装

402

GitHub Stars

126

下载量

3,280
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:auto-paper-demo(自动纸张演示)
来源仓库:https://github.com/marimo-team/skills
仓库路径:skills/auto-paper-demo
安装命令:
npx skills add https://github.com/marimo-team/skills --skill auto-paper-demo
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/marimo-team/skills --skill auto-paper-demo

简介

auto-paper-demo 从学术论文提取核心观点并转化为演示文稿,突出创新点与应用价值。

  • 适合科研人员快速制作报告素材,或产品经理理解技术亮点用于业务宣讲。
  • 通过 alphaxiv.org 获取结构化论文内容,避免直接解析 PDF 带来的格式错乱问题。
  • 输出包含标题、副标题与 Markdown 卡片的 YAML 文件,适配多种展示模板渲染。
  • 注意区分学术严谨性与传播吸引力,适当软化表述但不得虚构实验结果或夸大结论范围。

SKILL.md

You need to come up with a compelling story to tell from a paper. Do not ask the user for feedback/input. You need to apply thinking and come up with the best story yourself.

Fetching Papers via AlphaXiv

Use alphaxiv.org to get structured, LLM-friendly paper content. This is faster and more reliable than trying to read a raw PDF.

Extract the paper ID

Parse the paper ID from whatever the user provides:

InputPaper ID
https://arxiv.org/abs/2401.123452401.12345
https://arxiv.org/pdf/2401.123452401.12345
https://alphaxiv.org/overview/2401.123452401.12345
2401.12345v22401.12345v2
2401.123452401.12345

Fetch the AI-generated overview (try this first)

curl -s "https://alphaxiv.org/overview/{PAPER_ID}.md"

Returns a structured, detailed analysis of the paper as plain markdown. One call, no JSON parsing.

Fetch the full paper text (fallback)

If the overview doesn't contain the specific detail you need (e.g., a particular equation, table, or proof):

curl -s "https://alphaxiv.org/abs/{PAPER_ID}.md"

Returns the full extracted text of the paper as markdown.

Error handling

  • 404 on the overview: Report hasn't been generated for this paper yet. Try the full text instead.
  • 404 on the full text: Text hasn't been processed yet. As a last resort, direct the user to the PDF at https://arxiv.org/pdf/{PAPER_ID}.
  • No authentication is required — these are public endpoints.

What is a good implementation?

A good implementation tells a story, that's the most important thing. The story should be simple, but it should not be missing.

Papers typically have more than one concept in them. So that means you need to pick a story! It isn't the goal to fully implement the paper or to rerun a giant benchmark. The goal is to take a lesson/idea and to explain that very clearly in a notebook that can simply run on a CPU. That way, a user can easily run learn something from it. When you look at the notebook, what is the main concept or idea that you think is worth exploring? What is the concept that tells a story?

Pick the idea that is easiest to explain with a minimum code example. For a minimum code example to really work, it tends to help to have one, maybe two charts to look at. Maybe there's a dropdown that lets you try out different settings. Possibly even a slider. But the one thing we would want to do is prevent that the user needs to do a lot of scrolling.

It will be typical that you'll want to compare two approaches. But take a moment to think about the example, because that matters most to the story. Most of the time you don't want to use a toy example. They're not informative and they are overdone. It may be better to generate a creative example that shows where one approach can really shine. We don't want to cherry pick, but we also don't want to do examples that have been overdone either.

I cannot stress enough how important it is to actually think about the story and the example before you write any code whatsoever. You should really ultra think this. Give the user some interaction but really try to prevent scrolling. A good example tells a story, it doesn't just state some facts.

Feel free to think about this decision, but once you've got it clear what idea is best to showcase, immediately proceed to build the marimo notebook.

Use the marimo-notebook skill for this, and possibly the anywidget skill, but only if a custom widget makes for a better story. If you strongly feel that it makes sense to use a custom anywidget, refer to references/ANYWIDGET.md.

When you are ready, make sure that you hide all the code and that you move the cells with inputs/outputs to the top of the file.

Example:

@app.cell
def _(hide_code=True):
    import marimo as mo
    return mo

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.68%
按下载量换算1,138

Claude

30.57%
按下载量换算1,003

Cursor

17.74%
按下载量换算582

Gemini CLI

9.37%
按下载量换算307

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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