Extract text, tables, and metadata from PDFs using Python libraries.
npx skills add https://github.com/endearqb/kimi-app --skill pdf-endearqb
Please help me install this Agent Skill. Skill: pdf Source: https://github.com/endearqb/kimi-app/tree/main/skills/pdf Command: npx skills add https://github.com/endearqb/kimi-app --skill pdf-endearqb
PDFs are often static and hard to extract data from, producing manual, error-prone workflows. This skill provides a comprehensive toolset to read, extract, transform, merge, and annotate PDFs to automate document tasks.
Run the included Python scripts to merge, split, extract text, and apply annotations to PDFs.
High-intent search queries and answers about installing and using this skill.
You can extract text and tables from PDF documents using Python libraries like pypdf and pdfplumber. These libraries read static PDF files and parse the structural layout to return editable text strings and structured table data for administrative workflows.
The best way to merge, split, and rotate PDF files programmatically is by using Python automation scripts with the pypdf library. These scripts manipulate document pages to combine multiple files, separate specific pages, and adjust orientation for report digitization.
You can process scanned PDFs and extract images using the pdf2image and Pillow libraries. These tools render PDF pages into images and handle image extraction, enabling searchable archives without requiring built-in text layers.
Yes, this PDF processing approach supports fillable forms handling. The included Python scripts can read, interact with, and transform both fillable and non-fillable forms to automate invoice processing and archival data retrieval workflows.
You should use pdfplumber for data extraction when you need precise table structure recognition. It accesses the exact positioning of text elements within PDF documents, providing accurate structured data extraction compared to basic text-only parsing libraries.
To apply watermarks and annotations to PDF pages, run the included Python scripts that utilize pypdf and Pillow. These scripts overlay text or image stamps onto document pages, transforming and securing PDFs for archival data retrieval.
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