What problem does it solve? Reading and citing PDFs accurately is hard: agents often paraphrase without page references, mishandle encrypted or scanned documents, or silently guess at scrambled layouts. This Skill extracts text and standard metadata from a PDF file or URL and anchors every answer to a specific PDF page number. ## Core Features & Use Cases - Page-cited extraction: Runs a bundled pypdf-based extractor that emits Markdown with ## PDF page N markers, so every claim in an answer traces to a source page. - Flexible input handling: Accepts local file paths or http(s) URLs, with digest-keyed download caching, a 200 MB size cap, page-range selection (e.g. 1-3,5,8-), and support for encrypted PDFs via an environment-variable password. - Honest limitation reporting: Detects scanned or image-only pages (no OCR), flags ambiguous multi-column layouts, and uses a strict exit-code contract (0 success, 1 fix input, 2 retryable download failure). - Use Case: Ask an agent to summarize a 60-page research paper PDF and quote the methodology section; the response cites exact PDF pages and discloses any pages with no extractable text. ## Quick Start Ask the agent to read the PDF at a given path or URL and answer your question with page citations, for example: summarize paper.pdf and quote the key findings with their page numbers.