Paper Recommendation Skill

# 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：
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