scholar-skill

To build a knowledge network from academic papers in Obsidian, you can use this Skill to automatically extract semantic, episodic, and procedural memories from PDFs and link them into a comprehensive knowledge map. It relies on Python scripts to manage workflows and structured notes without manual effort.

L1, L2, and L3 academic paper reading levels represent graded reading support: L1 facilitates quick screening, L2 enables standard comprehension, and L3 provides deep analysis. This allows researchers to efficiently process dozens of papers at varying depths based on their immediate needs.

You can automatically generate structured notes from a research PDF by asking the AI to process the file at a specific reading level, such as 'Read the file paper.pdf at L2 level'. The Skill then extracts key memories and updates your knowledge graph accordingly.

Yes, this academic paper reading Skill requires Obsidian to function, as it integrates directly with the platform to build a dynamic, connected understanding of your research. It relies on the obsidian-direct dependency to manage structured notes and knowledge graph linkages.

Yes, you can use arXiv papers with this memory extraction and knowledge linking workflow because the Skill includes the arxiv-watcher dependency. This allows researchers to automatically monitor, fetch, and process academic papers into their evolving knowledge network.

The best way to process dozens of research papers without manual note-taking is to use this Skill's graded reading support and automatic memory extraction. It updates and connects your existing insights in Obsidian, fostering a dynamic research environment with minimal manual intervention.