medical-research

The Medical Research skill serves as a dedicated research assistant for navigating complex biomedical literature. It bridges the gap between dense, technical scientific findings and actionable, everyday understanding. Designed for students, healthcare professionals, and anyone curious about scientific advancement, this tool automates the process of finding peer-reviewed papers and synthesizing their core concepts into accessible language.

• Automatically queries the PubMed database using the built-in pubmed_search.py script.

• Synthesizes findings across multiple papers to identify key research themes, clinical implications, and emerging directions in medicine.

• Translates complex jargon into a 10th-grade reading level, defining specialized medical terminology to ensure comprehension.

• Structures research summaries into comprehensive markdown documents, including sections for overviews, clinical impacts, and future challenges.

• Provides a transparent overview of the research landscape, including citation of PMIDs, publication dates, and author contributions.

• Triggers automatically when users express interest in specific diseases, treatment approaches, drug development, or general scientific inquiries.

• Requires natural language input; for best results, specify the target condition or therapeutic area (e.g., "immunotherapy for melanoma").

• Includes mandatory disclaimers, noting that outputs are for educational purposes and do not constitute professional medical advice.

• Saves results locally in the project environment using a standardized naming convention (Research_Summary_{Topic}_{YYYY-MM-DD}.md) for easy retrieval and versioning.

• Adheres to strict quality guidelines, prioritizing currency (recent 1-2 years), objectivity, and balanced reporting of both breakthroughs and persistent knowledge gaps.