Paper Recommendation

Paper Recommendation Skill

自动发现、深度阅读、生成简报 - 你的AI论文研究助手

A Clawdbot skill for AI research paper discovery, review, and recommendation.

Overview

This skill provides automated paper fetching, sub-agent review, and recommendation generation for AI research papers. It follows a complete workflow from arXiv paper discovery to detailed briefing generation.

Features

• Automatic Paper Discovery: Fetch latest papers from arXiv by category and keywords
• Parallel Review: Use sub-agents to read and review multiple papers simultaneously
• Structured Output: Generate detailed briefings with consistent format
• Daily Automation: Cron job support for daily paper research

Scripts

1. fetch_papers.py

Fetches latest papers from arXiv and optionally downloads PDFs.

Usage:

# Fetch papers only
python3 scripts/fetch_papers.py --json

# Fetch and download PDFs
python3 scripts/fetch_papers.py --download --json

Output:

{
  "papers": [...],
  "total": 15,
  "fetched_at": "2026-01-29T17:00:00Z",
  "papers_dir": "/home/ubuntu/jarvis-research/papers",
  "pdfs_downloaded": ["/path/to/paper.pdf"]
}

2. review_papers.py

Generates sub-agent tasks for parallel paper review.

Usage:

# With papers from fetch_papers.py
python3 scripts/fetch_papers.py --json | python3 scripts/review_papers.py --json

# Or directly
python3 scripts/review_papers.py --papers '<json-string>' --json

Output:

{
  "papers": [...],
  "subagent_tasks": [
    {
      "paper_id": "2601.19082",
      "task": "请完整阅读这篇论文并给出评分...",
      "label": "review-2601.19082"
    },
    ...
  ],
  "count": 5,
  "instructions": "使用 sessions_spawn 开子代理..."
}

3. read_pdf.py

Reads PDF files and extracts text for analysis.

Usage:

# Extract text from PDF
python3 scripts/read_pdf.py ~/jarvis-research/papers/2601.19082.pdf

# Extract and output JSON
python3 scripts/read_pdf.py ~/jarvis-research/papers/2601.19082.pdf --json

# Extract specific sections (abstract, experiments, etc.)
python3 scripts/read_pdf.py ~/jarvis-research/papers/2601.19082.pdf --sections --json

Output:

{
  "success": true,
  "pdf_path": "/home/ubuntu/jarvis-research/papers/2601.19082.pdf",
  "text_length": 15000,
  "text": "Full PDF text...",
  "sections": {
    "abstract": "Abstract text...",
    "methodology": "Methodology text...",
    "experiments": "Experiments text...",
    "results": "Results text...",
    "conclusion": "Conclusion text..."
  },
  "extracted_at": "2026-01-29T17:00:00Z"
}

Note: Uses pdftotext (Poppler) for PDF text extraction.

Jarvis's Workflow (Agent Actions)

When you ask Jarvis to research papers, Jarvis should:

Step 1: Call fetch_papers.py

python3 scripts/fetch_papers.py --download --json

Step 2: Review the papers

Examine the paper list and decide which to review.

Step 3: Generate sub-agent tasks

python3 scripts/review_papers.py --papers '<papers-json>' --json

Step 4: Spawn sub-agents for paper review

For each paper, spawn a sub-agent to read and review:

# Example: Spawn one sub-agent per paper
clawdbot sessions spawn \
  --task "请完整阅读这篇论文并给出评分：..." \
  --label "review-2601.19082"

Sub-agent task requirements:

• Read the full paper via arXiv HTML page
• Extract: institutions, full abstract, contributions, conclusions, experiments
• Score: 1-5
• Recommend: yes/no
• Reply with JSON format

Step 5: Collect reviews and decide

• Collect all sub-agent results
• Analyze scores and recommendations
• Jarvis makes final decision (score >= 4 && recommended == yes)

Step 6: Generate detailed briefing

Create a comprehensive briefing following the Standard Briefing Format (see below).

Step 7: Deliver

Send the briefing via Telegram or other channels.

📋 Standard Briefing Format (Required)

All briefings MUST follow this exact format. No exceptions.

Mandatory Structure

# 📚 论文简报 - TOPIC | YYYY年MM月DD日

---

## 📄 PAPER_TITLE

**标题:** Full paper title (英文原标题)  
**作者:** Author1, Author2, Author3... (所有作者，用逗号分隔)  
**机构:** Institution1; Institution2; Institution3... (真实机构名，不是作者名)  
**arXiv:** https://arxiv.org/abs/xxxx.xxxxx  
**PDF:** https://arxiv.org/pdf/xxxx.xxxxx.pdf  
**发布日期:** YYYY-MM-DD | **分类:** cs.XX (arXiv 分类)

### 摘要
Chinese translation of the abstract (full paragraph, ~200-400 characters). 必须是完整的中文翻译，不能是摘要片段。

### 核心贡献
1. Contribution 1 (一句话概括核心贡献)
2. Contribution 2
3. Contribution 3 (2-4个贡献点)

### 主要结论
1. Conclusion 1 (一句话概括主要结论)
2. Conclusion 2 (2-4个结论点)

### 实验结果
• Experiment setup 1 (实验设置)
• Experiment setup 2
• Key finding 1 (关键发现)
• Key finding 2 (3-5个要点)

### Jarvis 笔记
- **评分:** ⭐⭐⭐⭐ (X/5)
- **推荐度:** ⭐⭐⭐⭐⭐
- **适合研究方向:** Field1, Field2 (1-2个研究方向)
- **重要性:** One sentence summary (一句话说明为什么重要)

---

## 📊 统计
- 论文总数: N
- 平均评分: ⭐⭐⭐⭐ (X/5)
- 推荐指数: ⭐⭐⭐⭐⭐

---
*Generated by Jarvis | YYYY-MM-DD HH:MM | TOPIC*

⏰ Daily Workflow (Cron Job)

自动执行时间: 每天 10:00 AM

Add Cron Job (Clawdbot)

# 添加每日完整论文调研任务
clawdbot cron add \
  --name "daily-paper-research" \
  --description "每日完整论文调研：获取→阅读→简报→发送" \
  --cron "0 10 * * *" \
  --system-event "请执行完整论文调研工作流：运行 python3 /home/ubuntu/skills/jarvis-research/scripts/daily_workflow.py。这会获取具身智能论文、下载 PDF、生成简报并发送到我的 Telegram。完成后告诉我结果。" \
  --deliver \
  --channel telegram \
  --to 8077045709

Check Status

# 列出所有 cron 任务
clawdbot cron list

# 查看任务详情
clawdbot cron status

What It Does

每天 10:00 AM 自动执行完整工作流：

1. 获取论文 - 从 arXiv 获取具身智能相关论文（前 6 篇）
2. 下载 PDF - 下载所有论文的 PDF 文件
3. 生成简报 - 按标准格式生成论文简报
4. 发送 Telegram - 发送摘要到用户 Telegram

Workflow Script

# 手动执行完整工作流
python3 /home/ubuntu/skills/jarvis-research/scripts/daily_workflow.py

Output Files

• 简报: ~/jarvis-research/papers/briefing-embodied-{YYYY-MM-DD}.md
• PDF 文件: ~/jarvis-research/papers/{paper-id}.pdf
• Telegram: 摘要自动发送到用户

Notes

• Cron 触发 Agent 执行 daily_workflow.py
• 脚本自动完成：获取 → 下载 → 生成 → 发送
• Agent 收到结果后可以继续深入分析（可选）

Topics

默认主题: 具身智能 (Embodied Intelligence)

关键词配置在 scripts/fetch_papers.py:

KEYWORDS = [
    'embodied', 'embodiment', 'embodied intelligence', 'embodied AI',
    'robotics', 'robot', 'manipulation', 'grasping',
    'vision-language-action', 'VLA', 'VLN',
    'reinforcement learning', 'sim2real', 'domain randomization',
    'sensorimotor', 'perception', 'motor control', 'action',
    'physical intelligence', 'embodied navigation'
]

Field Definitions & Rules

Critical Rules ⚠️

1. 机构 must be real institutions - Fetch from arXiv HTML page (/abs/<id>), NOT author names
2. 摘要 must be Chinese - Full translation from English abstract, not fragments
3. 实验结果 required - Must include experimental setup AND key findings
4. One paper per section - Each paper gets its own ## 📄 section
5. All fields required - Never skip any field
6. No placeholders - Replace all example text with actual content

How to Get Information

For institutions and authors:

# Fetch arXiv HTML page (recommended)
curl https://arxiv.org/abs/<paper-id>

# Or use web_fetch tool
web_fetch --url https://arxiv.org/abs/<paper-id> --extractMode text

For full abstract and content:

# Fetch HTML full text
curl https://arxiv.org/html/<paper-id>

For PDF (if available):

# Download and extract text
pdftotext <paper-id>.pdf -

Example Agent Prompt

When you want Jarvis to research papers:

请执行论文调研任务：
1. 调用 fetch_papers.py 获取今天的多智能体相关论文（带 PDF 下载）
2. 查看论文列表，决定哪些值得深入阅读
3. 调用 review_papers.py 生成子代理任务
4. 使用 sessions_spawn 为每篇论文开一个子代理，要求：
   - 完整阅读论文（arXiv HTML 页面）
   - 提取机构、中文摘要、核心贡献、主要结论、实验结果
   - 给出 1-5 评分和推荐
   - 回复 JSON 格式
5. 收集所有子代理结果，分析评分，选出 3-5 篇推荐论文
6. 为每篇生成详细简报（必须包含：标题、作者、机构、中文摘要、核心贡献、主要结论、实验结果、Jarvis笔记）
7. 发送到我的 Telegram

Configuration

Papers Directory: ~/jarvis-research/papers/

Categories Monitored:

• cs.AI (Artificial Intelligence)
• cs.LG (Machine Learning)
• cs.MA (Multi-Agent Systems)

Keywords: multi-agent, agent, collaboration, coordination, task planning, llm, reasoning, autonomous, swarm, collective, reinforcement, hierarchical, distributed, emergent

Sub-agent Model:

• Default: inherits from main agent
• Can override via agents.defaults.subagents.model or sessions_spawn.model

Notes

• Skills are tools - Jarvis uses them as needed
• Jarvis makes all decisions (which papers to review, which to recommend)
• Sub-agents do parallel paper reading (faster than sequential)
• Skills output structured data - Jarvis interprets and acts on it
• The briefing is Jarvis's creative work - not automated
• Always follow the Standard Briefing Format - Never deviate

Files

~/skills/paper-recommendation/
├── SKILL.md              # This file (FULL DOCUMENTATION)
└── scripts/
    ├── fetch_papers.py   # Paper fetching + PDF download
    ├── review_papers.py  # Sub-agent task generation
    └── read_pdf.py       # PDF text extraction

PDF Reading:

• Uses pdftotext (Poppler) for text extraction
• Can extract full text or specific sections (abstract, experiments, etc.)
• Useful for sub-agents to read downloaded PDFs

Paper Recommendation Skill - AI Research Assistant