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

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： 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/<id>PDFDirect PDF URL✅https://arxiv.org/pdf/<id>.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/<id>), 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/<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