How does it handle tables and figures?

The skill is specifically optimized for table extraction (even with merged cells) and can extract and describe figures when using the MinerU VLM mode.

Does this skill require an API key?

It functions best with a MinerU API key for high-complexity documents, but includes robust fallback options for manual conversion and simple text extraction.

Can it process academic papers with multi-column layouts?

Yes, its VLM-based parsing is designed to handle multi-column academic formats and mathematical formulas that traditional OCR often fails to capture.

What is the difference between VLM and Pipeline mode?

VLM Mode uses a vision-language model for 90%+ accuracy at a slower speed, while Pipeline mode uses traditional parsing for faster results on simpler documents.

Intelligent Document Conversion

Name: Intelligent Document Conversion
Author: linxule

bylinxule

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Content Management

Converts complex PDFs and academic papers into structured text using intelligent tool selection and MinerU integration.

This skill streamlines the process of importing complex documents into your research workflow by automatically selecting the most effective conversion method. It intelligently toggles between high-accuracy VLM-based parsing via MinerU for academic papers containing tables and figures, and lightweight manual methods for straightforward text documents. By handling layout preservation, mathematical formulas, and multi-column formatting, it ensures that researchers and developers can quickly transform unstructured PDF data into high-quality, actionable text for qualitative analysis.

Key Features

012 GitHub stars

02Automated extraction of tables, figures, and mathematical formulas

03Built-in quality assurance checklist for verifying extraction integrity

04High-accuracy MinerU integration with 90%+ accuracy VLM mode

05Batch processing capabilities for handling multiple documents simultaneously

06Intelligent tool selection based on document complexity and layout

Use Cases

01Extracting structured data from complex reports for AI-assisted literature reviews

02Bulk converting PDF field notes and transcripts for qualitative research streams

03Importing multi-column academic papers with complex data tables for analysis

Key Features

012 GitHub stars

02Automated extraction of tables, figures, and mathematical formulas

03Built-in quality assurance checklist for verifying extraction integrity

04High-accuracy MinerU integration with 90%+ accuracy VLM mode

05Batch processing capabilities for handling multiple documents simultaneously

06Intelligent tool selection based on document complexity and layout

Use Cases

01Extracting structured data from complex reports for AI-assisted literature reviews

02Bulk converting PDF field notes and transcripts for qualitative research streams

03Importing multi-column academic papers with complex data tables for analysis