Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Content Creation
Daily Paper Digest
每日 AI 论文速递，自动聚合 arXiv 和 HuggingFace 的最新论文并推送到聊天应用。
skill openclawclawhub Free
每日 AI 论文速递，自动聚合 arXiv 和 HuggingFace 的最新论文并推送到聊天应用。
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, arxiv_fetcher.py, config/sources.json, config/sources_llm.json, huggingface_fetcher.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- qjymary
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
📚 每日 AI 论文速递
每天自动从 arXiv 和 HuggingFace 抓取最新 AI 论文，格式化后推送到你的聊天应用（飞书、Slack、Discord 等）。
fetch_daily_papers
获取今日最新论文速递。 用法： python3 main.py 参数： 无（自动读取 config/sources.json 中的配置） 返回： 格式化的论文列表，包含标题、作者、摘要、链接
search_arxiv_papers
搜索特定主题的 arXiv 论文。 用法： python3 arxiv_fetcher.py 参数（在代码中修改）： query：搜索关键词，如 "large language model" max_results：最大返回数量（默认 5） 返回： 匹配的论文列表
fetch_huggingface_papers
获取 HuggingFace 每日热门论文。 用法： python3 huggingface_fetcher.py 参数： 无（直接爬取 https://huggingface.co/papers） 返回： 热门论文列表，含点赞数
配置
编辑 config/sources.json 来自定义信息源和过滤规则： { "sources": [ { "name": "arxiv", "enabled": true, "categories": ["cs.AI", "cs.CL", "cs.CV", "cs.LG"], "max_results": 10 }, { "name": "huggingface", "enabled": true, "max_results": 10 } ], "filter": { "keywords": ["LLM", "transformer"], "exclude_keywords": [] } }
arXiv 常用分类
代码含义cs.AI人工智能cs.CL计算语言学/NLPcs.CV计算机视觉cs.LG机器学习cs.NE神经网络cs.RO机器人stat.ML统计机器学习
1. 安装依赖
pip3 install -r requirements.txt
2. 运行测试
python3 test.py
3. 获取今日论文
python3 main.py
4. 定时自动运行（配合 OpenClaw 调度器）
在 OpenClaw 中配置 Cron 表达式（例如每天 9:00）： 0 9 * * *
在 OpenClaw 中触发
在聊天应用中发送以下任意内容即可触发： 论文速递 今日论文 最新论文 /papers /digest
依赖
arxiv — arXiv 官方 Python 客户端 requests — HTTP 请求 beautifulsoup4 — HTML 解析 feedparser — RSS/Atom 解析
示例输出
╔══════════════════════════════════════════════════════════╗ ║ 🎓 AI 论文每日速递 - 2026年02月20日 ║ ╚══════════════════════════════════════════════════════════╝ 📊 今日共收录 15 篇论文 ============================================================ 📄 论文 1 ============================================================ 📌 标题: Attention Is All You Need 👥 作者: Ashish Vaswani, Noam Shazeer 等 8 人 🏷️ 来源: ARXIV | 日期: 2026-02-20 📝 摘要: The dominant sequence transduction models are based on... 🔗 arXiv: http://arxiv.org/abs/1706.03762 📥 PDF: http://arxiv.org/pdf/1706.03762
文件结构
daily-paper-digest/ ├── SKILL.md ← 本文件（ClawHub 规范） ├── main.py ← 主程序 ├── arxiv_fetcher.py ← arXiv 模块 ├── huggingface_fetcher.py ← HuggingFace 模块 ├── requirements.txt ← Python 依赖 └── config/ ├── sources.json ← 默认配置 └── sources_llm.json ← LLM 专用配置
Category context
Writing, remixing, publishing, visual generation, and marketing content production.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts2 Config
- SKILL.md
- README.md
- arxiv_fetcher.py
- huggingface_fetcher.py
- config/sources_llm.json
- config/sources.json