What problem does it solve? Literature discovery for papers, theses, and surveys often relies on memory or scattered manual searches, producing unverified citations. This Skill grounds academic search in real Semantic Scholar API responses, returning structured, deduplicated paper metadata ready for review. ## Core Features & Use Cases - Paper Search & Lookup: Run relevance-ranked, boolean bulk, full-text snippet, and exact-title searches, plus direct lookup by DOI, arXiv, PMID, or CorpusId. - Citation Graph & Recommendations: Traverse citing and referenced papers with intent labels, and get single-seed or multi-seed similar-paper recommendations. - Author Tools & Export: Search authors, list their papers, and export results as Markdown tables, JSON, or BibTeX with source_status and next_action fields for literature matrices. - Use Case: While writing a related-work section, ask for papers on a topic since 2018; the Skill runs one Python script against the API, deduplicates results, and returns a ranked table with DOIs, citation counts, and follow-up actions for Zotero and Scite. ## Quick Start Use the semanticscholar-skill to find recent papers on gamma spectrum classification with machine learning and export the top results as BibTeX.