literature-survey

To survey AI/ML literature across multiple databases, this Skill queries arXiv, Semantic Scholar, and DBLP to fetch metadata, deduplicate results, and classify papers into structured themes. It synthesizes high-quality literature to guide your research directions.

Yes, you can automatically generate BibTeX entries for AI/ML papers. The Skill fetches metadata and BibTeX entries across arXiv, Semantic Scholar, and DBLP sources, deduplicating the results to provide ready-to-use references for publication or proposal writing.

The best way to identify research gaps in machine learning literature is through systematic gap analysis and cross-domain exploration. This Skill detects existing gaps, explores cross-domain transfers, and proposes 2-3 actionable directions with feasibility assessments.

Yes, this literature survey tool requires the Python dependencies requests and arxiv to function. These libraries enable the Skill to query academic databases, fetch metadata, and retrieve BibTeX entries for your specified AI/ML topic.

To get a structured report from an AI literature survey, you simply provide a research topic. The Skill classifies papers into themes, summarizes findings, detects gaps, and outputs a structured report with references ready for publication or proposal writing.