# Send Paper Recommendation to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "paper-recommendation", "name": "Paper Recommendation", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/SJF-ECNU/paper-recommendation", "canonicalUrl": "https://clawhub.ai/SJF-ECNU/paper-recommendation", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/paper-recommendation", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=paper-recommendation", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "WORKFLOW.md", "scripts/daily_workflow.py", "scripts/fetch_papers.py", "scripts/read_pdf.py", "scripts/review_papers.py" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/paper-recommendation" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/paper-recommendation", "downloadUrl": "https://openagent3.xyz/downloads/paper-recommendation", "agentUrl": "https://openagent3.xyz/skills/paper-recommendation/agent", "manifestUrl": "https://openagent3.xyz/skills/paper-recommendation/agent.json", "briefUrl": "https://openagent3.xyz/skills/paper-recommendation/agent.md" } } ``` ## Documentation ### 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 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 '' --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/PDFDirect PDF URL✅https://arxiv.org/pdf/.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/), 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/ # Or use web_fetch tool web_fetch --url https://arxiv.org/abs/ --extractMode text For full abstract and content: # Fetch HTML full text curl https://arxiv.org/html/ For PDF (if available): # Download and extract text pdftotext .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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: SJF-ECNU - Version: 1.0.1 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-30T16:55:25.780Z - Expires at: 2026-05-07T16:55:25.780Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/paper-recommendation) - [Send to Agent page](https://openagent3.xyz/skills/paper-recommendation/agent) - [JSON manifest](https://openagent3.xyz/skills/paper-recommendation/agent.json) - [Markdown brief](https://openagent3.xyz/skills/paper-recommendation/agent.md) - [Download page](https://openagent3.xyz/downloads/paper-recommendation)