What problem does it solve? Conducting a rigorous literature review requires searching multiple academic databases, screening dozens of papers, verifying every citation, and synthesizing findings into a structured document — a process that is slow, error-prone, and vulnerable to fabricated references. ## Core Features & Use Cases - Multi-database search: Queries PubMed, arXiv, Semantic Scholar, CrossRef, and Scopus with documented search strategies and PRISMA flow tracking. - Citation verification: Every DOI is validated via CrossRef and every arXiv ID is resolved before inclusion, preventing hallucinated references. - Structured synthesis: Produces thematic analysis, comparison matrices, evidence-tier grading, research gap identification, and mandatory PRISMA or thematic diagrams. - Use Case: A graduate student needs a systematic review of rTMS for depression covering 2018-2026. The skill searches PubMed and Semantic Scholar, screens 391 hits down to 7 key papers, grades evidence strength, and outputs a PRISMA-compliant review with verified DOIs. ## Quick Start Ask the AI to conduct a systematic literature review on your research question, specifying the domain, time range, and whether you want a PRISMA-compliant output.
