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To automate arXiv paper discovery and generate daily notes in Obsidian, this Skill queries arXiv for recent AI/ML papers, scores them by relevance and recency, and outputs a dated daily note with a top-10 list and wikilinked references.

Deduplication for new research papers works by scanning your existing Obsidian vault notes to avoid duplicate entries, building a keyword-to-note index that ensures only newly discovered papers are added to your daily journal.

Yes, you can use Python scripts to fetch and score AI/ML papers by relevance and recency. The workflow utilizes scripts like search_arxiv.py alongside requests and PyYAML to query semantic sources and format results.

The best way to create a connected research journal with wikilinked references is to use a skill that supports keyword linking and note referencing, automatically building a navigable index between your dated daily notes and related literature.

Yes, you need an existing Obsidian vault to generate daily paper recommendations. The workflow operates within this environment to scan previously generated notes for deduplication and output correctly formatted wikilinks.

Daily notes may miss linked references for newly scored papers if the keyword-to-note index fails to map terms correctly. The link_keywords.py script processes these mappings to ensure proper note referencing and navigability.