Paper Recommendation

What This Skill Does

The Paper Recommendation skill is an automated research assistant designed to streamline the discovery and analysis of AI research papers from arXiv. It functions as an end-to-end pipeline that fetches the latest research, manages parallel sub-agent reviews, and extracts deep insights from PDF content. By automating the tedious process of monitoring academic feeds and manually summarizing dense technical documentation, it allows users to stay at the cutting edge of AI development with minimal overhead.

Installation

To integrate this skill into your OpenClaw environment, use the following command:

clawhub install openclaw/skills/skills/sjf-ecnu/paper-recommendation

Ensure that your system environment has pdftotext (Poppler) installed, as the skill relies on this utility for high-fidelity text extraction from academic PDFs.

Use Cases

• Automated Research Aggregation: Set up daily cron jobs to fetch and summarize papers on specific topics like 'Large Language Models' or 'Computer Vision' so you wake up to a curated briefing.
• Technical Due Diligence: Use the multi-agent review feature to get structured summaries of multiple papers, making it easier to compare methodologies and experimental results across different publications.
• PDF Knowledge Extraction: Extract specific sections like 'Methodology' or 'Experiments' from a local library of papers for quick referencing without reading the entire document.

Example Prompts

1. "Jarvis, check for the latest papers on 'Reinforcement Learning' from arXiv, download the top 3, and provide a summary for each."
2. "Review the paper at ~/jarvis-research/papers/2601.19082.pdf and extract the methodology and experimental results sections for me."
3. "Run a daily research report on the latest papers in the 'cs.LG' category and save the structured briefing to my research folder."

Tips & Limitations

• System Dependencies: Ensure the pdftotext library is correctly linked in your PATH for the read_pdf.py script to function.
• Performance: While the skill supports parallel sub-agent reviews, monitor your local resource usage when processing large batches of papers to avoid memory bottlenecks.
• Scope: The skill is optimized for arXiv. If you need to search other databases, consider customizing the fetch_papers.py script to include additional API sources.