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OpenClaw

Arxiv Paper Reader

利用python,指定某个arxiv_id/url, 基于 LLM Agent 对这篇arxiv论文进行分类与深度阅读,直接print打印阅读笔记

aurora-14120 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install aurora-1412/arxiv-reader

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

快速开始

1. 配置 .env

cp .env.example .env   # 或直接编辑 .env

确定你已经配置了:

  • LLM_API_KEY — OpenAI 或兼容 API 的密钥
  • LLM_BASE_URL — API 地址

2. 运行

uv venv
uv pip install -r "{baseDir}/requirements.txt"

# 单篇论文模式:指定 arxiv_id 或 URL
uv run python "{baseDir}/main.py" --arxiv-id 2401.12345
uv run python "{baseDir}/main.py" --arxiv-id https://arxiv.org/abs/2401.12345
uv run python "{baseDir}/main.py" --arxiv-id https://arxiv.org/pdf/2401.12345.pdf

# 指定以特定类别阅读
uv run python "{baseDir}/main.py" --arxiv-id xxxx --category yyy

# 查看所有类别
uv run python "{baseDir}/main.py" --list

添加新的阅读分类

在 skills/ 下新建文件夹,包含两个文件:

skills/your_new_category/
├── _metadata.md        # 分类描述(告诉 Classifier 什么论文属于这个类别)
└── reading_prompt.md   # 阅读指南(告诉 Reader Agent 重点关注什么)

重启即可自动识别,无需修改任何代码。

Python包

  • LangChain 1.x — Agent 框架(基于 LangGraph)
  • LangChain OpenAI — LLM 接口(兼容 DeepSeek 等 OpenAI-compatible API)
  • arxiv — 官方 Python 库
  • arxiv-to-prompt 获取arxiv论文latex源码

Source information

Source
github
Repository
openclaw/skills
Path
skills/aurora-1412/arxiv-reader
Version
—
Commit
9e2ac9088c4cf21400d5a8b164b395b5bb31ee6d
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
## 快速开始

### 1. 配置 `.env`

```bash
cp .env.example .env   # 或直接编辑 .env
```

确定你已经配置了:
- `LLM_API_KEY` — OpenAI 或兼容 API 的密钥
- `LLM_BASE_URL` — API 地址

### 2. 运行

```bash
uv venv
uv pip install -r "{baseDir}/requirements.txt"

# 单篇论文模式:指定 arxiv_id 或 URL
uv run python "{baseDir}/main.py" --arxiv-id 2401.12345
uv run python "{baseDir}/main.py" --arxiv-id https://arxiv.org/abs/2401.12345
uv run python "{baseDir}/main.py" --arxiv-id https://arxiv.org/pdf/2401.12345.pdf

# 指定以特定类别阅读
uv run python "{baseDir}/main.py" --arxiv-id xxxx --category yyy

# 查看所有类别
uv run python "{baseDir}/main.py" --list

```

## 添加新的阅读分类

在 `skills/` 下新建文件夹,包含两个文件:

```
skills/your_new_category/
├── _metadata.md        # 分类描述(告诉 Classifier 什么论文属于这个类别)
└── reading_prompt.md   # 阅读指南(告诉 Reader Agent 重点关注什么)
```

重启即可自动识别,无需修改任何代码。

## Python包

- LangChain 1.x — Agent 框架(基于 LangGraph)
- LangChain OpenAI — LLM 接口(兼容 DeepSeek 等 OpenAI-compatible API)
- arxiv — 官方 Python 库
- arxiv-to-prompt 获取arxiv论文latex源码

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