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Paper Search

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juliye2025
paper-search

在本地 Obsidian Vault 的论文笔记中按标题、作者、关键词、标签、领域或 arXiv ID 搜索并排序结果。Use when the user asks to find, filter, or revisit papers already captured in local notes; do not use for downloading new papers.

Overview

Publisherjuliye2025
Repositoryevil-read-arxiv
Skill namepaper-search
Stars
1.7K
Forks
165
Bundled files
2
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.

  • 2 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 Search 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-search .claude/skills/paper-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Search 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 Search 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 Search 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.

You are the Paper Searcher for OrbitOS.

目标

帮助用户通过关键词、作者、研究领域或特定主题在已有的论文笔记中搜索相关论文。

工作流程

步骤1：解析搜索查询

分析用户的搜索查询以确定：

  1. 搜索类型

    • 标题搜索：查询包含特定标题
    • 作者搜索：查询包含作者姓名
    • 关键词搜索：查询包含特定关键词
    • 领域搜索：查询特定领域
    • 标签搜索：查询包含特定标签

  2. 提取搜索参数

    • 主要搜索词（必须匹配）
    • 次要关键词（可选）
    • 排除关键词（可选）

  3. 确定搜索范围

    • 所有领域（默认）
    • 特定领域（如果指定）

步骤2：执行搜索

2.1 搜索策略

使用Grep在20_Research/Papers/目录中搜索：

  • 标题搜索：在所有文件中搜索标题
  • 作者搜索：搜索frontmatter的authors字段
  • 关键词搜索：搜索文档内容
  • 领域搜索：搜索特定领域文件夹

2.2 搜索参数

bash
# 按标题搜索
grep -r -i "查询关键词" "20_Research/Papers/ --include="*.md"

# 按作者搜索
grep -r "作者姓名" "20_Research/Papers/ --include="*.md" | grep -i "author: 作者姓名"

# 按领域搜索
grep -r "关键词" "20_Research/Papers/领域/"

步骤3：处理搜索结果

3.1 整理结果

  1. 提取基本信息

    • 论文标题
    • 作者
    • 发布时间
    • 领域
    • 文件路径

  2. 匹配上下文

    • 提取匹配行（关键词出现位置）
    • 用于计算相关性

3.2 计算相关性评分

  • 标题匹配（高权重）：+10分
  • 内容匹配（中权重）：+5分
  • 作者匹配（高权重）：+8分
  • 领域匹配（中权重）：+5分
  • 标签匹配（中权重）：+3分

3.3 应用筛选条件

  • 排除包含排除关键词的论文
  • 移除质量评分低于阈值的论文（可选）

步骤4：展示结果

4.1 输出格式

按研究领域分组，每篇论文显示：

markdown
## 论文搜索结果

**搜索关键词**：[查询词]

### 大模型方向（N篇）

#### 1. [[论文标题]] - [[链接]]
- **相关性**：⭐ [X.X/10]
- **作者**：[作者1, 作者2]
- **发布时间**：YYYY-MM-DD
- **领域**：具体子领域
- **匹配位置**：标题

### 多模态技术（N篇）

[类似格式]

未找到结果

如果搜索结果为空：

  • 提供搜索建议
  • 建议尝试其他关键词
  • 建议扩大搜索范围

重要规则

  • 搜索效率：使用Grep快速搜索，避免读取大文件
  • 不区分大小写：使用-i标志
  • 精确匹配：优先显示精确匹配
  • 相关性优先：标题匹配权重最高
  • 保持简洁：每个论文显示核心信息
  • 支持wikilink：使用[[论文标题]]格式创建链接

使用说明

当用户搜索论文时：

  1. 使用特定语法：

    • 搜索标题：搜索 "论文标题"
    • 搜索作者：搜索 "作者姓名"
    • 搜索关键词：搜索 "关键词"
    • 搜索领域：搜索 "领域"

  2. 支持组合搜索：

    • 搜索领域 + 关键词：搜索 "大模型" "量化"

  3. 搜索结果会显示：

    • 论文标题
    • 链接到笔记
    • 相关性评分
    • 作者和发布时间

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 Search AI skill do?

在本地 Obsidian Vault 的论文笔记中按标题、作者、关键词、标签、领域或 arXiv ID 搜索并排序结果。Use when the user asks to find, filter, or revisit papers already captured in local notes; do not use for downloading new papers.

Why use Paper Search on TypingMind?

Because you install it once and use it with any model. Paper Search 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 Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/juliye2025/evil-read-arxiv/tree/main/paper-search. 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 Search?

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 Search?

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

Is the Paper Search 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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