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Paper Analyze

CommunityPopular
juliye2025
paper-analyze

深度分析 arXiv 论文、公开 PDF URL、本地 PDF、研究项目页、技术博客或普通网页，自动识别来源类型并生成带证据与图片的 Obsidian 笔记。Use when the user provides an arXiv ID/link、public PDF URL、company research page、blog URL、local PDF or existing note and asks for analysis, summary, evaluation, or knowledge-base capture.

Overview

Publisherjuliye2025
Repositoryevil-read-arxiv
Skill namepaper-analyze
Stars
1.7K
Forks
165
Bundled files
4
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by juliye2025 on GitHub. Read the source before you install it.

Installation

Install the Paper Analyze AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/juliye2025/evil-read-arxiv.git /tmp/evil-read-arxiv
mkdir -p .claude/skills
cp -r /tmp/evil-read-arxiv/paper-analyze .claude/skills/paper-analyze
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Analyze in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Paper Analyze on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Paper Analyze is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

多来源深度分析

先确定来源类型，再选择论文或网页分析结构。不得因输入含数字就假定是 arXiv，也不得给博客、公司报告或普通 PDF 伪造 arXiv 元数据。

解析路径

  1. 将当前 SKILL.md 的父目录视为 。
  2. 从 OBSIDIAN_VAULT_PATH 解析 ；缺失时要求用户提供 Vault 路径。
  3. 验证 /20_Research/Papers，读取研究配置中的 language；缺失时使用 zh。
  4. 在系统临时目录创建本次 ，不在 skill 目录或 Vault 中保存临时下载。

识别来源

始终先运行：

<python> "<skill-dir>/scripts/resolve_source.py" --input "<user-input>" --work-dir "<work>" --output "<work>/source.json"

读取 source.json，并按 source_type 分支：

  • arxiv：仅当输入是合法的 YYMM.NNNN/NNNNN、arXiv:ID 或 arxiv.org 链接时使用。保留 arxiv_id、摘要页和 PDF 链接。
  • pdf_url：公开 URL 返回 PDF 内容或 PDF Content-Type。使用原始 URL，分析下载到 的 local_pdf。
  • local_pdf：分析用户提供的本地 PDF。
  • project_page：研究项目页或公司页面中发现明确的 Paper/PDF 链接。联合分析页面与自动下载的 selected_pdf_url；同时保留项目页和 PDF URL。
  • blog：具有文章日期/作者元数据，或 URL 路径含 blog/posts/article 的 HTML。按博客结构分析，不套论文评审模板。
  • webpage：其他公开 HTML 页面。按网页资料分析并标注证据边界。
  • title：既不是路径、URL，也不是 arXiv ID 的文本。仅在用户确实只给标题时再搜索一手来源。

若公开页面依赖 JavaScript，resolver 提取不完整时使用 当前 agent 的网页访问能力读取页面；仍以 source.json 的 URL 和来源类型为准。

获取与查重

  1. 在 Vault 中按 document_id、arxiv_id、规范化标题和 source_url 查重。
  2. 已有笔记时保留手工内容，只局部补充。
  3. 论文/PDF 分支以 PDF 原文为主要证据；项目页可补充作者、机构、代码和演示。
  4. blog/webpage 分支以页面正文为主要证据；区分作者主张、引用证据与 agent 推断。
  5. 无法访问全文时只分析可见内容，并明确说明缺失范围。

图片

对 arxiv、pdf_url 或 local_pdf，需要图片时读取相邻 ../extract-paper-images/SKILL.md，并把 arXiv ID、直接 PDF URL 或 local_pdf 传给脚本。

对 blog/project_page/webpage，只保存能帮助理解内容且允许公开访问的页面图片；不要把导航图标、头像或品牌 logo 当作研究插图。

生成笔记

仅在目标笔记不存在时创建模板：

<python> "<skill-dir>/scripts/generate_note.py" --title "<title>" --authors "<authors>" --domain "<domain>" --vault "<vault>" --language "<zh|en>" --source-type "<source_type>" --source-url "<source_url>" --document-id "<document_id>" --paper-id "<arxiv_id>" --published-date "<published_date>" --venue "<venue>"

模板分支：

  • arxiv/pdf_url/local_pdf：研究文档结构，覆盖问题、方法、公式、实验、局限、相关工作和评分。
  • blog/project_page/webpage：资料结构，覆盖核心论点、内容脉络、关键证据、技术细节、可信度、局限和实践启示；不要求会议、引用数或论文 ID。

替换全部占位符后再更新图谱：

<python> "<skill-dir>/scripts/update_graph.py" --title "<title>" --domain "<domain>" --score <0-10> --vault "<vault>" --language "<zh|en>" --source-type "<source_type>" --source-url "<source_url>" --paper-id "<arxiv_id>" --document-id "<document_id>"

格式与证据规则

  • frontmatter 至少保留 document_id、source_type、source_url、title、authors、published、domain。
  • 只有 arxiv 类型才写 arxiv_id 与 arXiv/PDF 派生链接。
  • 直接 PDF、博客与网页始终保留用户给出的原始 URL；重定向后的 canonical_url 可另外记录。
  • 公式使用 Markdown LaTeX；图片使用 ![[filename.png|800]]；笔记链接使用 [[File_Name|Display Title]]。
  • 未知作者、日期、机构、会议或数字写 --，不猜测。
  • 不覆盖用户笔记，不删除旧图片，不把网页主张改写成已经复现的事实。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Paper Analyze AI skill do?

深度分析 arXiv 论文、公开 PDF URL、本地 PDF、研究项目页、技术博客或普通网页，自动识别来源类型并生成带证据与图片的 Obsidian 笔记。Use when the user provides an arXiv ID/link、public PDF URL、company research page、blog URL、local PDF or existing note and asks for analysis, summary, evaluation, or knowledge-base capture.

Why use Paper Analyze on TypingMind?

Because you install it once and use it with any model. Paper Analyze is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Paper Analyze in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/juliye2025/evil-read-arxiv/tree/main/paper-analyze. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Analyze?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Paper Analyze?

As many as you like. As long as a model supports skills, you can use Paper Analyze with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper Analyze AI skill free?

It is published on GitHub by juliye2025. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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