literature-review

To conduct a systematic literature review, this Skill searches arXiv and Semantic Scholar to find papers, then applies structured screening and extraction to organize methods and results. This yields a synthesized literature map with citations and gap analysis.

The best way to map the research landscape involves thematic synthesis across academic papers to reveal consensus and tensions. This process outputs a structured extraction highlighting underexplored areas, making gap analysis and scoping reviews highly efficient.

Yes, you can perform structured data extraction for questions, methods, and results across discovered papers. This enables direct cross-paper comparison, providing the organized thematic synthesis needed to craft a rigorous related-work section.

You provide a defined research question, and the Skill executes a structured search, screening, and extraction phase. It organizes the scholarly literature thematically, outputting a narrative-ready synthesis with citations suitable for scoping reviews.

This approach is tailored for AI and ML domains, applying systematic discovery and extraction across sources like arXiv and Semantic Scholar. While optimized for AI research, its thematic synthesis mechanism can technically process general scholarly literature.

A structured extraction replaces manual searching by automating the organization of questions, methods, and references. It prevents missing critical papers during landscape mapping and directly outputs a tabular synthesis with gap analysis, saving research time.