ingesting-academic-content

To extract metadata and citations from academic PDFs, the Skill applies multi-format parsing and structure recognition to identify titles, authors, dates, keywords, and bibliographies, returning a machine-readable JSON or Markdown export.

Yes, you can extract document structure from DOCX and HTML lecture notes. The Skill recognizes chapters, sections, and references across DOCX, HTML, Markdown, and web pages for downstream analytics.

The best way to ingest multi-format academic content for reference managers is using a tool that parses diverse sources and exports structured JSON containing metadata, outlines, and extracted citations suitable for direct ingestion.

Yes, it works with scanned PDFs requiring OCR by utilizing Tesseract.js alongside pdf-parse and pdf2pic dependencies to extract text and structure from image-based academic documents.

Academic documents that can be classified and processed include textbooks, papers, lecture notes, and assignments. The Skill extracts key concepts and classifies the document type during ingestion.

To parse web pages for academic citations and metadata, the Skill uses node-fetch and jsdom with Readability to extract titles, authors, dates, and in-text citations from online academic content into structured formats.