Building an AI Agent Skill for Multi-Database Literature Collection

Literature search is one of the most foundational steps in medical research. Before protocol design, evidence synthesis, peer review, or manuscript writing, researchers must first identify the right body of evidence. A weak literature search process creates downstream problems: missing key studies, incomplete evidence mapping, duplicated screening effort, and poor reproducibility in review workflows. This becomes even critical in biomedical research, where studies are distributed across multiple databases such as PubMed, CrossRef, OpenAlex, Semantic Scholar, and specialized repositories.