Extract text, tables, and metadata from PDF documents.
npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill pdf-nferrer-dev
Please help me install this Agent Skill. Skill: pdf Source: https://github.com/nferrer-dev/claude-dotfiles/tree/main/skills/pdf Command: npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill pdf-nferrer-dev
This skill solves the challenge of programmatically interacting with PDF documents, which are often difficult to parse or modify without specialized software.
Use the pdf skill to extract all tables from the document named report.pdf and save them into a new Excel file.
High-intent search queries and answers about installing and using this skill.
To extract tables from a PDF and save them to Excel, you can use this skill to pull structured table data via pdfplumber and pass it through pandas for dataframe conversion. This automates the transformation of multi-page financial or operational reports into spreadsheet-ready formats.
Yes, you can use OCR to extract text from scanned PDF documents. This skill leverages pytesseract and pdf2image to process image-based PDFs, converting visual document pages into machine-readable text for downstream data processing and automated workflows.
The best way to merge and split PDF files programmatically is using pypdf within this skill. It provides robust utilities for document manipulation, allowing you to combine multiple PDFs into a single file or extract specific pages without manual editing software.
Yes, this PDF extraction tool supports password-protecting documents. Beyond data extraction, it provides comprehensive document manipulation utilities including encryption, allowing you to secure sensitive files directly through automated Python workflows.
To generate automated PDF reports from extracted data, this skill utilizes reportlab for programmatic document creation. You can compile extracted text and tables into newly formatted PDF files, enabling end-to-end automated report generation workflows.
While pdfplumber handles structured table extraction from complex PDF layouts, scanned or image-heavy documents require OCR via pytesseract, which may lose some formatting precision. For highly irregular layouts, combining OCR text extraction with manual pandas structuring yields the best results.
xongtaoExtract plain text from images using the Tesseract CLI with multilingual support.
run6270Analyze and manipulate Excel spreadsheet data with pandas and openpyxl.
HanLi0705Extract text and tables from PDFs into structured data formats.
galeepExtract text and metadata from PDFs, DOCX, and images with spatial bounding boxes.
