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:** ```bash # Fetch papers only python3 scripts/fetch_papers.py --json

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

**Output:** ```json { "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:** ```bash # 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:** ```json { "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:** ```bash # 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:** ```json { "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 ```bash 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 ```bash 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:

```bash # 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

```markdown # 📚 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)

```bash # 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

```bash # 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

```bash # 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`: ```python 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 ⚠️

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

**For PDF (if available):** ```bash # 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

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*Paper Recommendation Skill - AI Research Assistant*