pubmed-search

Search PubMed and export structured metadata by providing a query with MeSH terms and filters. The skill uses PubMed eSearch and eFetch APIs to retrieve ranked results, then saves a TSV file containing abstracts, authors, and citations for literature reviews.

Yes, you can filter biomedical literature searches by date range, journal type, publication type, and study type. These filters apply directly to PubMed queries, ensuring the returned metadata and abstracts match specific systematic review or topic scouting requirements.

Visualize keyword trends and publication timelines from PubMed results by running the skill, which automatically generates visualizations using matplotlib. These charts accompany a formatted markdown report and a TSV file of extracted biomedical literature data.

Yes, generating formatted reports and visualizations from PubMed literature searches requires the pandas and matplotlib dependencies. The requests library is also needed to interface with the PubMed eSearch and eFetch APIs for data extraction.

MeSH terms improve biomedical literature search results in PubMed by enabling targeted querying of the medical subject heading hierarchy. This structured approach retrieves highly relevant papers with accurate metadata, abstracts, and citations for systematic searches.

Limitations of using PubMed eSearch and eFetch APIs include retrieving large result sets with limited initial metadata, which can slow down literature reviews. The skill mitigates this by structuring the extracted data into TSV files and formatted markdown reports for efficient processing.