What is the mathpix-ocr skill?

It is a specialized Claude Code skill that utilizes the Mathpix API to extract mathematical formulas and structural data from images and PDFs, converting them into LaTeX or ACSet formats.

What are balanced ternary checkpoints?

Balanced ternary checkpoints are a resilient processing pattern used by this skill to manage large PDF batches, allowing the system to recover from errors and maintain high confidence during extraction.

How does it integrate with ACSets?

The skill includes a mapping layer that identifies mathematical constructs in the extracted LaTeX and structures them into ACSets for use in algebraic Julia and parallel rewriting systems.

Does this skill require a Mathpix account?

Yes, you must provide your own Mathpix API ID and Key within the MCP server configuration to use the OCR capabilities.

Mathpix OCR & LaTeX Pipeline

Name: Mathpix OCR & LaTeX Pipeline
Author: plurigrid

byplurigrid

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Data Science & ML

Extracts LaTeX and structural ACSet data from mathematical documents using Mathpix and balanced ternary checkpoints.

About

This skill integrates the Mathpix API into Claude Code to provide high-fidelity mathematical OCR and structured data extraction. It specializes in converting images and PDFs into LaTeX ASTs and ACSet schemas, specifically optimized for Homotopy Type Theory (HoTT) and category theory applications. By utilizing a unique balanced ternary checkpoint system (Seed 1069), it ensures resilient batch processing of complex scientific documents while providing deep integration with topological computing workflows and sonification feedback for real-time monitoring.

Key Features

Advanced detection of HoTT constructs including dependent types, transport, and univalence
Sonification support for audio-based feedback on batch processing status and confidence
2 GitHub stars
High-fidelity LaTeX extraction from images and PDFs via Mathpix API integration
Automatic mapping of LaTeX constructs to ACSet (Algebraic Julia) type structures
Resilient batch processing using 7-trit balanced ternary checkpoints for error recovery

Use Cases

Implementing resilient OCR pipelines for large-scale scientific PDF archives with checkpoint-based recovery
Automating the extraction of category theory diagrams and spectral sequences for scientific research
Digitizing mathematical textbooks into machine-readable LaTeX and formal type-theoretic structures

About

Key Features

Advanced detection of HoTT constructs including dependent types, transport, and univalence
Sonification support for audio-based feedback on batch processing status and confidence
2 GitHub stars
High-fidelity LaTeX extraction from images and PDFs via Mathpix API integration
Automatic mapping of LaTeX constructs to ACSet (Algebraic Julia) type structures
Resilient batch processing using 7-trit balanced ternary checkpoints for error recovery

Use Cases

Implementing resilient OCR pipelines for large-scale scientific PDF archives with checkpoint-based recovery
Automating the extraction of category theory diagrams and spectral sequences for scientific research
Digitizing mathematical textbooks into machine-readable LaTeX and formal type-theoretic structures