What problem does it solve? Biomedical research requires precise, reproducible literature searches that general web search cannot deliver. This Skill provides structured PubMed query construction, MeSH term usage, and direct NCBI E-utilities API workflows so searches are accurate and repeatable. ## Core Features & Use Cases - Query Construction: Build PubMed search strings using field tags ([ti], [ab], [mh], [pt], [dp]), Boolean operators, MeSH terms, and subheadings. - E-utilities API Workflow: Run esearch, esummary, efetch, and elink programmatically from Python with rate limiting, history server support, and API key handling. - Reproducible Search Logging: Record exact search strings, dates, filters, and result counts for systematic reviews. - Use Case: A researcher conducting a systematic review on type 2 diabetes treatments can build a MeSH-based query, retrieve PMIDs via esearch, fetch abstracts via efetch, and log every search pass for the review protocol. ## Quick Start Use the pubmed-database skill to find randomized controlled trials on hypertension published from 2024 to 2026 and return the PMIDs.