Parse, transform, and extract data from PDF documents automatically.
npx skills add https://github.com/Postalocity/template-microsite --skill pdf-postalocity
Please help me install this Agent Skill. Skill: pdf Source: https://github.com/Postalocity/template-microsite/tree/main/.opencode/skills/anthropic-skills--pdf Command: npx skills add https://github.com/Postalocity/template-microsite --skill pdf-postalocity
PDF processing tasks are time-consuming and error-prone when done manually, including text extraction, table extraction, merging or splitting files, filling forms, and applying OCR to scans.
Process a PDF to extract text and tables, merge or split documents, fill forms, and OCR scanned pages.
High-intent search queries and answers about installing and using this skill.
Automated PDF text and table extraction parses document content using pdfplumber and pypdf to transform unstructured pages into structured data workflows. It handles digital and scanned PDFs, enabling downstream data analysis and reporting.
PDF form filling supports both interactive and non-fillable forms. The process validates inputs and annotates pages, allowing you to automate administrative form completion tasks across various document types.
OCR for scanned PDFs converts image-based pages into searchable and indexable text using pdf2image and Pillow. This transforms static scans into machine-readable documents for data extraction and retrieval.
Merging and splitting PDFs organizes documents and prepares batches by extracting specific pages or combining files. This automated processing uses pypdf to restructure documents for administrative workflows.
This PDF processing approach satisfies production-ready workflows by leveraging pypdf, pdfplumber, pdf2image, and Pillow. It delivers end-to-end document transformation with validation across administrative tasks.
PDF table extraction fails on scanned documents because scanned pages are images without embedded text layers. Applying OCR via pdf2image and Pillow first converts scans into searchable text before extraction.
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