domains / families / documents / pdf-extract

pdf-extract

combined

Extract text from PDF files for LLM processing Extract PDF selectable text and full-page or segmented page images (including tables) into Markdown with per-page headings and image links; use when you need both readable text and page visuals for PPT creation, review, or analysis.

When to use

• Converting a PDF report/paper into Markdown while preserving page structure (`## Page XX`) for easy navigation.
• Preparing PPT or design materials where you need both extracted text and page screenshots/blocks (tables, figures, diagrams).
• Reviewing scanned or mixed PDFs and filtering out "text-only" screenshots to keep only meaningful visuals.
• Building a dataset for downstream analysis where each page's text and images must be linked and traceable.
• Creating a lightweight "PDF-to-Markdown" archive with per-page headings and image references.

When not to use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Skill page

Conflict

The two skills share the name 'pdf-extract' but implement fundamentally different procedures. The first (LeoYeAI) is a thin wrapper around the `pdftotext` binary (poppler-utils) for simple text extraction. The second (aipoch) is a complex Python-based workflow using `pdfplumber` and `pymupdf` that extracts text and images, normalizes paragraphs, and outputs a structured Markdown file with per-page headings and image links. They use different dependencies, different commands, and produce different outputs (plain text vs. structured Markdown with images). Crowning one would hide the other's specific utility (e.g., image extraction and Markdown structuring vs. simple text dumping).