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To automate daily paper recommendations, this Skill queries arXiv and Semantic Scholar to filter recent papers based on your configured research interests, scoring them to generate structured Markdown notes.

Yes, the daily paper recommendation workflow optionally extracts the first image from the top three recommended papers, embedding them directly into the generated Markdown notes for quick visual reference.

You can link new research paper keywords to existing notes using the deterministic Python pipeline, which maps extracted terms to your current files, forming a navigable knowledge network.

No manual API configuration is needed during execution; the pipeline relies on a customizable YAML file to define research interests and handles arXiv API and Semantic Scholar queries automatically.

The best way to track recent scholarly papers without manual searching is to trigger the automated pipeline, which scans recent publications, scores them against your YAML interests, and outputs a dated Markdown recommendation file.

A limitation of this Semantic Scholar and arXiv search pipeline is its deterministic nature, meaning it relies strictly on predefined YAML configurations for scoring and filtering without dynamic, AI-driven query adjustments.