Finds and synthesizes academic research papers on longevity, aging, and lifespan extension from Semantic Scholar.
Accelerates deep research into longevity and aging by providing targeted access to the Semantic Scholar academic database. This skill automates the process of finding relevant scientific papers based on user queries, with options to filter by recency, citation count, and date ranges. It intelligently refines search terms, synthesizes key findings from the most relevant articles into a natural language summary, and provides a structured list of sources with abstracts and links for further investigation.
Key Features
01Synthesizes key findings from multiple papers into a natural language summary
02Filters research by date, year, and citation count
03Searches the Semantic Scholar API for academic papers
04Generates a structured citation list with titles, links, citation counts, and abstracts
05Uses an automated three-query retry logic for comprehensive searches
0632 GitHub stars
Use Cases
01Discovering the latest research on a specific longevity intervention (e.g., rapamycin).
02Finding the most cited or foundational papers in a particular area of aging research.
03Compiling a list of relevant academic articles from a specific time period.
