# 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

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 '' --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： 获取论文 - 从 arXiv 获取具身智能相关论文（前 6 篇） 下载 PDF - 下载所有论文的 PDF 文件 生成简报 - 按标准格式生成论文简报 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 FieldDescriptionRequiredRules标题Full paper title✅英文原标题，不要翻译作者All authors✅用逗号分隔，所有作者机构Real institutions✅必须是真正的机构名，从 arXiv HTML 页面提取，绝对不能是作者名arXivarXiv abstract URL✅https://arxiv.org/abs/PDFDirect PDF URL✅https://arxiv.org/pdf/.pdf发布日期Publication date✅YYYY-MM-DD 格式分类arXiv category✅e.g., cs.RO, cs.AI摘要Chinese translation✅完整翻译，不是片段，~200-400字符核心贡献Core contributions✅2-4 个 bullet points，一句话 each主要结论Main conclusions✅2-4 个 bullet points，一句话 each实验结果Experimental results✅必须有，3-5 个要点，包含设置和关键发现Jarvis 笔记Jarvis assessment✅评分、推荐度、研究方向、重要性 Critical Rules ⚠️ 机构 must be real institutions - Fetch from arXiv HTML page (/abs/), NOT author names 摘要 must be Chinese - Full translation from English abstract, not fragments 实验结果 required - Must include experimental setup AND key findings One paper per section - Each paper gets its own ## 📄 section All fields required - Never skip any field 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/ --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 .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