What makes the Mathpix OCR skill different from standard OCR?

Unlike standard OCR, this skill identifies complex mathematical constructs (HoTT) and maps them into ACSets (Algebraic Databases) while using a balanced ternary system for high-reliability batch processing.

How does the 'Seed 1069' checkpoint system work?

Seed 1069 uses a 7-trit balanced ternary pattern to manage extraction phases, moving between high-confidence, exploration, and recovery states to ensure resilient processing of large document batches.

Can I use this for multi-page PDF documents?

Yes, the skill includes 'smart_pdf_batch' and 'batch_convert' tools specifically designed to handle large PDFs with auto-chunking and progress tracking.

Does it require a Mathpix API key?

Yes, you must provide your MATHPIX_APP_ID and MATHPIX_APP_KEY in the MCP server configuration to enable the OCR capabilities.

What mathematical frameworks are supported for extraction?

It is optimized for Homotopy Type Theory (HoTT), category theory (topos diagrams), and general LaTeX-based mathematical notation.

Mathpix OCR & ACSet Extraction

Name: Mathpix OCR & ACSet Extraction
Author: plurigrid

byplurigrid

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Data Science & ML

Converts mathematical documents and images into structured LaTeX and ACSet data models using resilient balanced ternary checkpoints.

The Mathpix OCR skill bridges the gap between static mathematical PDFs and dynamic computational structures. By integrating the Mathpix API with the music-topos ACSet rewriting system, it allows researchers and developers to extract LaTeX, identify Homotopy Type Theory (HoTT) constructs, and map them directly into Algebraic Databases (ACSets). It features a unique 7-trit balanced ternary checkpoint system (Seed 1069) for reliable batch processing and provides sonification feedback to monitor extraction confidence and progress in real-time.

Key Features

01High-accuracy LaTeX extraction from images and complex PDF documents

02Direct mapping of mathematical structures into ACSet algebraic databases

03Resilient batch processing using Seed 1069 balanced ternary checkpoints

047 GitHub stars

05Real-time sonification feedback and Vision Pro P3 color mapping for extraction monitoring

06Automatic detection of HoTT constructs including dependent types and univalence

Use Cases

01Digitizing complex mathematical textbooks into computable type-theoretic structures

02Automating the extraction of topos diagrams and spectral sequences from research papers

03Building searchable, structured databases of mathematical constructs from legacy PDF archives

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi mathpix-ocr

For use in Claude.ai and ChatGPT

Download Skill