Generates daily HuggingFace Papers research reports with detailed paper analysis and Bitable integration
Automatically generates comprehensive daily research reports from HuggingFace Papers, including:
When user requests daily HuggingFace paper report or similar:
tavily_search to find HuggingFace Daily Papers for the target date
tavily_extract to get the full paper list from the HF pagefeishu_doc tool to create cloud documentfeishu_doc(action="append") to write ALL content in blocksfeishu_doc(action="read") to confirm content is written (block_count > 50)message tool to send report with document link (ONLY after verification passes)read action before sending messageBoth message and document (default):
Message only (when user asks for quick summary):
Document only (when user asks to save without sending):
# Generate report for today
node scripts/generate_report.js
# Generate report for specific date
node scripts/generate_report.js --date 2026-02-22
# Create Feishu document
node scripts/create_document.js --title "Report Title"
YYYY-MM-DD Hugging Face Daily Papers Report
# Hugging Face Daily Papers Report
## 目录
- [1. Paper Title 1](#1-paper-title-1)
- [2. Paper Title 2](#2-paper-title-2)
- ...
- [N. 今日趋势总结](#n-今日趋势总结)
---
## 1. Paper Title
**🏢 机构**: Institution Name
**📅 提交日期**: YYYY 年 M 月 D 日
**🔥 热度**: 当日最热 / 高度关注 / 持续上升 / 新兴热点
**🔬 研究方向**: [研究方向,例如:Agentic RL, Multimodal, Spatial Reasoning, Code Generation, etc.]
**📌 核心贡献**:
详细描述核心贡献(2-3 句话)。必须清晰说明论文的主要创新点和解决的问题。
**🔬 关键技术**:
- **技术中文名(English Name)**: 描述该技术的作用和原理(1-2 句话)
- **技术中文名(English Name)**: 描述该技术的作用和原理(1-2 句话)
- **技术中文名(English Name)**: 描述该技术的作用和原理(1-2 句话)
> ⚠️ **注意**:
> 1. 关键技术不能只写关键词,必须包含简短的解释说明
> 2. 技术名称必须使用**中文(英文)**格式,例如:自进化 AI 社会(Self-Evolving AI Society)
> 3. 每项技术需要说明其作用和在论文中的具体应用
**📊 实验结果**:
- 具体的实验结果和性能指标
- 与 baseline 的对比数据
- 关键发现和洞察
**🔗 链接**:
- HF Papers 地址:https://huggingface.co/papers/...(必需)
- arXiv 地址:https://arxiv.org/abs/...
- PDF 地址:https://arxiv.org/pdf/...
- 项目地址:https://...
---
## N. 今日趋势总结
1. **Trend 1**: Description
2. **Trend 2**: Description
3. **Trend 3**: Description
---
*报告由蛋仔 🐰 自动生成*
Each paper should include:
{
title: "Paper Title",
institution: "Institution Name",
date: "YYYY-MM-DD",
links: {
project: "https://...",
arxiv: "https://arxiv.org/abs/...",
pdf: "https://arxiv.org/pdf/...",
hfPapers: "https://huggingface.co/papers/..."
},
coreContribution: "Brief description",
keyTechniques: [
{ name: "Technique 1", description: "..." },
{ name: "Technique 2", description: "..." }
],
experimentalResults: [
"Result 1 with metrics",
"Result 2 with metrics"
]
}
For papers with structured data (e.g., model specifications, benchmark results):
feishu_bitable_create_app()feishu_bitable_create_record()## 📊 Model Specifications
**🔗 查看详细表格**: https://feishu.cn/base/APP_TOKEN
> 💡 **使用说明**: 这是一个实时更新的多维表格,支持筛选、排序和协作编辑。
tavily_search (PRIORITY #1): Search for HuggingFace Daily Papers
tavily_extract (PRIORITY #2): Extract paper list from HF Papers page
message: Send report directly to user (required - use action="send")feishu_doc: Create and format Feishu documents (required)feishu_bitable_create_app: Create Bitable for structured data (optional)feishu_bitable_create_field: Configure Bitable fieldsfeishu_bitable_create_record: Insert data into Bitableweb_search: Fallback search (requires Brave API key)web_fetch: Fallback extractionWhen sending via message tool:
action="send"target to reply to current conversationExample:
{
"action": "send",
"message": "📊 HuggingFace Daily Report - 2026-02-22\n\n【1. Paper Title - Institution】\n🔥 热度:当日最热\n🔬 方向:Agentic RL / Multimodal\n📌 核心:详细描述核心贡献(2-3 句话)\n🔬 技术:技术中文名 1(English,作用说明), 技术中文名 2(English,作用说明), 技术中文名 3(English,作用说明)\n📊 结果:具体的实验结果和性能指标\n🔗 HF Papers: https://huggingface.co/papers/...\n\n【2. Paper Title - Institution】\n🔥 热度:高度关注\n🔬 方向:Code Generation / Efficiency\n📌 核心:...\n🔬 技术:...\n📊 结果:...\n🔗 HF Papers: https://huggingface.co/papers/...\n\n📈 今日趋势:高效模型、具身智能成为热点\n\n📄 完整文档:https://feishu.cn/docx/...\n\n*报告由 蛋仔 🐰 整理*"
}
User: "生成 2026-02-17 的 HuggingFace 论文报告"
Assistant:
1. **TAVILY SEARCH** (PRIORITY):
- tavily_search(query="Hugging Face Daily Papers February 17 2026", n=10)
- Extract HF Papers URL from results (e.g., https://huggingface.co/papers/date/2026-02-17)
2. **TAVILY EXTRACT**:
- tavily_extract(url="https://huggingface.co/papers/date/2026-02-17")
- Get complete paper list with titles, institutions, upvotes, comments
3. **SELECT TOP 5+ PAPERS**:
- Sort by upvotes/comments (popularity)
- **MUST include at least top 5 papers**
- Extract HF Papers link for each (e.g., /papers/2602.10809)
4. **EXTRACT DETAILS**: For each of top 5+ papers:
- Get arXiv ID from HF link
- Extract institution, core contribution, key techniques, results
5. **CREATE DOCUMENT**: feishu_doc(action="create", title="2026-02-17 Hugging Face Daily Papers Report")
6. **WRITE CONTENT**: feishu_doc(action="append", ...) for each section
- Append TOC first
- Append each paper section (one append per paper)
- Append trends summary
7. **VERIFY**: feishu_doc(action="read", doc_token="...")
- Check block_count >= 50
- Verify all 5+ papers are included
8. **SEND MESSAGE WITH HF LINKS**: message(action="send", message="...")
- Include HF Papers link for EACH paper
- Include verified document URL
Message Output (WITH HF LINKS):
📊 HuggingFace Daily Papers Report - 2026-02-22
【1. SpargeAttention2 - 清华大学】
核心贡献:可训练稀疏注意力方法,动态选择关键 token 进行计算
关键技术:混合掩码规则(Hybrid Masking,结合局部和全局注意力)、高效 CUDA 实现(Efficient Implementation,GPU 优化加速)
实验结果:95% 稀疏度,16.2 倍加速,性能损失<1%
🔗 HF Papers: https://huggingface.co/papers/2602.13515
【2. Mobile-Agent-v3.5 - 阿里通义】
...
🔗 HF Papers: https://huggingface.co/papers/2602.16855
📈 今日趋势:
• Agent 方向持续火热
• 效率优化方案涌现
...
📄 完整文档:https://feishu.cn/docx/...
User: "把报告存到云文档,不用发给我"
Assistant:
1. Generate report content
2. Create Feishu document
3. Return only document link (no message send)
User: "快速看看今天有什么论文"
Assistant:
1. Fetch top 5 papers
2. Send condensed summary via message
3. Skip document creation
Select papers based on热度 (popularity) in this order:
Document First, Message Last (CRITICAL):
feishu_doc(action="create")feishu_doc(action="append") callsfeishu_doc(action="read") - check block_count >= 50Paper Selection: Focus on top 8-10 trending papers
Detail Level: Include all key information (institution, date, links, contributions, techniques, results)
Link Format: Use clear labels (项目地址,arXiv 地址,etc.)
HF Papers Link in Message (REQUIRED):
🔗 HF Papers: https://huggingface.co/papers/...Trend Analysis: Summarize 3-5 key trends at the end
Document Structure: Use numbered headings (## 1., ## 2., etc.)
Table of Contents: Auto-generate for documents with 3+ sections
Bitable Usage: Use for structured data that benefits from filtering/sorting
Message Formatting:
Verification Checklist (before sending message):
block_count >= 50 verified via read action