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

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

主要结论

Conclusion 1 (一句话概括主要结论)
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

Field	Description	Required	Rules
`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
`arXiv`	arXiv abstract URL	✅	`https://arxiv.org/abs/<id>`
`PDF`	Direct 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/&lt;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

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

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