PDF Structure Extraction

PDFs are a display format, not a data format — characters on the page have no intrinsic reading order or semantic role. Pure text extraction loses tables, flow, and citation links. A layout-aware pipeline (bounding-box clustering + heuristic heading detection) recovers the author's intended structure 90%+ of the time, which is exactly what an LLM needs to cite accurately. The skill packages those heuristics as a single callable unit so the LLM can stay focused on reasoning rather than parsing.

1) pdfplumber extracts character boxes + font sizes per page. 2) Lines are clustered by baseline y-coordinate, then columns by x-histogram. 3) Text in the largest font on a page is tagged as a heading; superscript numeric runs are tagged as footnote anchors. 4) Tabular regions are detected via horizontal/vertical ruling-line density and passed to camelot-lite. 5) Output is a JSON tree: {sections: [{heading, level, text, figures, footnotes}]}. 6) If per-page character count < threshold, the page is re-run through Tesseract OCR before step 1 — catches scanned pages transparently.