Crop exam PDF questions into separate WebP images organized by exam name.
npx skills add https://github.com/1000ssam/skills-for-teachers --skill exam-analyzer
Please help me install this Agent Skill. Skill: exam-analyzer Source: https://github.com/1000ssam/skills-for-teachers/tree/main/skills/exam-analyzer Command: npx skills add https://github.com/1000ssam/skills-for-teachers --skill exam-analyzer
Processing and organizing large volumes of exam PDFs is time-consuming and error-prone; manual cropping of individual questions is labor-intensive and inconsistent.
Process the provided exam PDFs to crop each question into separate images.
High-intent search queries and answers about installing and using this skill.
You can crop exam questions from a PDF into separate images by processing the file with automated page segmentation. The tool identifies each question and exports it as a WebP image organized by exam name.
Yes, you can extract exam questions using natural language topic specification. When a textbook is unavailable, this flexible analysis feature matches questions based on your descriptive topic input.
Yes, this tool works with Korean national exams including 수능, 모평, and 학평. It automatically analyzes these assessment PDFs to identify and crop each question into separate WebP images.
Processing and cropping exam PDFs requires the Python packages pymupdf, pillow, and numpy. These dependencies handle PDF text extraction, image processing, and array operations for robust page segmentation.
The best way to organize cropped exam questions for archiving is grouping the output WebP images by exam name. The tool automatically structures the output crops this way during the page segmentation process.
Keyword-driven matching works by extracting precise keywords from an available textbook to identify relevant questions in the exam PDF. This enables accurate targeted matching when you have a specific textbook reference.
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