Convert mixed PDFs with text, images, and charts into Markdown.
npx skills add https://github.com/taturou/pdf-to-md-skill --skill pdf-to-md
Please help me install this Agent Skill. Skill: pdf-to-md Source: https://github.com/taturou/pdf-to-md-skill/tree/main/.codex/skills/pdf-to-md Command: npx skills add https://github.com/taturou/pdf-to-md-skill --skill pdf-to-md
Converts mixed PDFs containing text, images, and charts into AI-friendly Markdown for easier understanding, indexing, and processing.
Convert a batch of PDFs by running the pipeline to output one Markdown file per document with images in an images/ directory.
High-intent search queries and answers about installing and using this skill.
Batch processing converts mixed PDFs into Markdown by running an end-to-end pipeline that includes text extraction, OCR, and figure handling. It preserves headings, tables, and figures to ensure structured, AI-friendly output for downstream indexing.
Batch processing multiple PDFs for AI indexing is handled through chunked job management that processes many documents with varying layouts in one run. This outputs one Markdown file per document with linked images for searchability.
PDF to Markdown conversion requires image processing dependencies including pdf2image and pillow. These libraries enable the pipeline to convert document pages into images for OCR and figure handling before schema-driven Markdown generation.
OCR handles text extraction from PDFs with varying layouts by first converting pages to images, then applying detection and extraction algorithms. This mechanism ensures consistent Markdown generation while preserving meaningful structure across different document types.
You can preserve tables and figures when turning PDF documents into Markdown through schema-driven generation. The pipeline handles figure extraction by placing images in an images directory and maintaining meaningful structural elements within the text.
Chunking large documents during PDF to Markdown conversion manages memory and processing limits by breaking jobs into smaller segments. While this handles large files effectively, extremely complex layouts may still require manual review for accurate structural preservation.
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