Paper Recommendation

Automates discovery, parallel review, scoring, and briefing generation of AI research papers from arXiv, supporting daily updates and PDF analysis.

Security Scan

The skill mostly matches its stated purpose (fetching, reading, and briefing arXiv papers) but contains a few incoherences and privacy/exfiltration risks you should review before installing.

Purpose & Capability

The skill's files and SKILL.md describe paper discovery, PDF extraction, spawning sub-agents, and delivering briefings — that matches the name. However the package metadata declares no required binaries or credentials while the scripts clearly call external CLIs (curl, pdftotext, clawdbot) and expect a local path layout (/home/ubuntu, ~/jarvis-research). The omission of required binaries in metadata is an inconsistency: installing this skill will require tools not declared.

Instruction Scope

Runtime instructions and scripts perform web requests to arXiv, download PDFs, extract text, spawn parallel sub-agents (via 'clawdbot sessions spawn') with full paper context, and send briefings to a hardcoded Telegram ID. These actions are within the high-level purpose but include steps that can transmit paper content and metadata to sub-agents or an external messaging endpoint — the SKILL.md also shows adding a cron job that will automate this. The presence of a hardcoded TELEGRAM_ID and an unused local GATEWAY_URL (127.0.0.1:18789) are notable and should be validated.

Install Mechanism

There is no install spec (instruction-only + included scripts). That is low-risk from an automatic installer perspective because nothing is fetched or executed at install time by the registry. However the delivered code will rely on system binaries (curl, pdftotext, clawdbot) when run.

Credentials

No environment variables or credentials are declared (primary credential: none), which is appropriate for a paper-fetching skill — but scripts hardcode a TELEGRAM_ID and default directories under /home/ubuntu and ~/jarvis-research. The skill will call 'clawdbot' CLI commands to spawn sessions and send messages; if clawdbot is configured with credentials or gateway tokens on the host, those will be used implicitly. The scripts do not request unrelated cloud credentials, but the hardcoded delivery target and implicit use of host 'clawdbot' credentials represent a privacy/exfiltration risk and a mismatch with the declared 'requires' metadata.

Persistence & Privilege

always:false (good). The SKILL.md and scripts provide explicit cron add examples that, if applied, create a persistent daily job that downloads PDFs and sends briefings to a Telegram account. The skill does not itself set system-wide configuration or modify other skills, but the provided automation instructions would create ongoing behavior if the user runs them. Autonomous spawning of sub-agents (the platform default) amplifies the impact of any data sent to those sub-agents.

What to consider before installing

What to check before installing or running this skill: - Metadata vs code: the registry metadata declares no required binaries, but the scripts call external tools (curl, pdftotext, clawdbot). Ensure those CLIs exist and understand their configuration on your host. - Hardcoded delivery target: daily_workflow.py contains a hardcoded TELEGRAM_ID (8077045709) and SKILL.md shows a cron example that will send briefings to that ID. Replace or remove this ID before enabling automation — otherwise you will send data to someone else's Telegram account. - Cron/automation: the skill provides instructions to add a daily cron job. Do not add the cron job until you review and trust the message destination and behavior; test the workflow manually first. - Sub-agents and data exposure: tasks passed to 'clawdbot sessions spawn' include paper content and may cause those contents to be processed by other agents/models. If your environment sends data to external LLM providers, this may leak paper text or internal notes. Inspect the exact 'task' payloads and verify where sub-agent processing occurs. - Local gateway URL: daily_workflow.py defines GATEWAY_URL = http://127.0.0.1:18789/api/message but does not use it; confirm there is no leftover code or hidden endpoints you don't expect. - Run in a sandbox: if you want to evaluate safely, run the scripts in a controlled environment (non-production VM or container) and point PAPERS_DIR to a directory you control. - Review and adapt: update paths, change/remove hardcoded TELEGRAM_ID, and audit how 'clawdbot' is configured on your machine (what credentials or gateways it uses) before enabling automated runs. In summary: the skill appears to implement its stated purpose, but metadata omissions, hardcoded recipient, and implicit use of host-level clawdbot credentials create privacy/exfiltration risks — treat as suspicious until you verify and sanitize configuration.

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：

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

# 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 ⚠️

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:

# 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

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Paper Recommendation Skill - AI Research Assistant