How does the skill ensure conversion quality?

It implements a specific Output Quality Checklist that prompts users to verify text accuracy, table structure, header integrity, and the readability of formulas and figures after the conversion process.

Do I need an API key to use this skill?

A MinerU API key is only required for high-fidelity automated parsing. The skill also provides fallback logic and guidance for manual conversion methods for simpler documents or when cost-saving is a priority.

Can this skill handle batch processing?

Yes, the skill includes logic for handling multiple documents simultaneously, allowing you to queue complex PDFs for MinerU batch processing while handling simpler ones via manual methods.

What is the benefit of using MinerU for PDF conversion?

MinerU uses vision-language models (VLM) to achieve over 90% accuracy, specifically excelling at extracting complex tables, multi-column layouts, and mathematical formulas that traditional parsers often fail to capture.

Qualitative Document Converter

Name: Qualitative Document Converter
Author: linxule

bylinxule

•

Data Science & ML

Automates high-fidelity PDF and document conversion by selecting the optimal parsing method for academic and qualitative research data.

The qual-convert skill is a specialized utility designed for researchers and developers who need to transform complex, unstructured documents into machine-readable formats. It intelligently navigates the trade-offs between speed, cost, and accuracy by choosing between high-fidelity AI parsing via MinerU and efficient manual conversion methods. Whether dealing with multi-column academic papers, intricate data tables, or mathematical formulas, this skill ensures that the structural integrity of the source document is preserved for downstream analysis in qualitative research workflows.

Key Features

01Intelligent tool selection based on document layout and complexity

02Automated quality checklist for post-conversion verification

03High-accuracy extraction of tables, figures, and formulas using MinerU VLM

04Preservation of multi-column layouts and structured academic formatting

05Batch processing support for large-scale document ingestion

062 GitHub stars

Use Cases

01Extracting structured data from scanned qualitative field notes or archived PDF documents

02Converting multi-column academic journals with complex tables for systematic literature reviews

03Optimizing document processing pipelines by balancing API costs with data accuracy requirements

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add linxule/interpretive-orchestration qual-convert

For use in Claude.ai and ChatGPT

Download Skill