literature-review

Automating a systematic literature review involves executing an end-to-end workflow that searches databases like PubMed and arXiv, screens results, extracts full-text data, and synthesizes findings into formatted markdown and PDF outputs.

Synthesizing state-of-the-art medical AI findings requires downloading full-text PDFs, extracting methodologies and datasets, verifying citations, and mapping evidence to produce professional academic reports with publisher-quality filtering.

Yes, you can search PubMed and arXiv simultaneously alongside Semantic Scholar and OpenAlex to screen biomedical, scientific, and technical literature with enforced publisher quality standards.

Full-text PDF downloads work by integrating with a download-paper skill to acquire complete documents, which are then read to extract methodologies, datasets, and results for evidence mapping.

Yes, the requests library is required as a dependency to execute automated multi-database literature searches, screen records, and generate verified citation outputs.

Completing a systematic literature survey generates well-formatted markdown reports and PDFs containing extracted data, quality assessments, references, and synthesis tables.