Convert scholarly PDFs into structured Obsidian notes with YAML frontmatter.
npx skills add https://github.com/Elephenman/paper-deep-read --skill paper-deep-read
Please help me install this Agent Skill. Skill: paper-deep-read Source: https://github.com/Elephenman/paper-deep-read/tree/main Command: npx skills add https://github.com/Elephenman/paper-deep-read --skill paper-deep-read
Paper Deep Read automates the extraction and interpretation of scientific literature, turning PDFs into structured Obsidian notes with cross-paper links, YAML frontmatter, and a scalable knowledge graph workflow.
Feed a PDF to Paper Deep Read to generate a complete Obsidian note in PaperVault.
High-intent search queries and answers about installing and using this skill.
To convert scholarly PDFs into structured Obsidian notes, this Skill applies a six-phase pipeline to extract text, figures, and tables, outputting Markdown files with YAML frontmatter, backlinks, and Dataview-ready structures directly into your PaperVault.
Yes, you can batch process multiple academic papers to build an Obsidian knowledge graph. The workflow executes QA loops and cross-paper concept linking, automatically organizing outputs into PaperVault and updating your Map of Content for scalable literature analysis.
PDF extraction for literature reviews uses PyMuPDF to capture full-text visual elements, isolating complex figures, tables, and formulas. This ensures reproducible analysis by preserving the structural and visual integrity of the original scholarly document within the generated notes.
Yes, you need PyMuPDF installed as a dependency to extract full-text and metadata from research papers. It provides the core PDF parsing capabilities required to identify visual elements and structure the extracted content into Obsidian-ready Markdown files.
The best way to create cross-paper links for a reproducible literature review is using an automated six-phase processing pipeline. It maps concepts across documents, standardizes metadata in YAML, and generates backlinks to construct a scalable, interconnected knowledge graph.
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