Can I use this skill for geometric deep learning?

Yes, it includes frameworks for equivariance and natural transformations, which are core components of geometric deep learning and naturality in AI.

How does the cats-for-ai skill improve AI architecture?

It introduces formal compositionality, allowing developers to treat neural network layers as mathematical morphisms that can be safely combined and reasoned about.

What are GF(3) trits in the context of this plugin?

GF(3) trits represent a three-valued logic system (-1, 0, +1) used for balancing states and representing 'Inaccessible Worlds' with maximal color saturation.

What is the relationship between lenses and backpropagation in this skill?

This skill models backpropagation as 'lens' composition, where the forward pass (view) and backward pass (update/gradient) are unified in a single bidirectional structure.

Categories for AI (cats-for-ai)

Name: Categories for AI (cats-for-ai)
Author: plurigrid

byplurigrid

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Data Science & ML

Integrates category theory principles into AI development to enable formal compositionality, bidirectional optics, and advanced machine learning architectures.

About

The cats-for-ai skill brings the formal rigor of category theory to the Claude Code environment, based on the authoritative cats.for.ai lecture series organized by researchers from DeepMind, Qualcomm, and top academic institutions. It provides Claude with a specialized framework for modeling neural networks as parametric spans, understanding backpropagation through the lens of bidirectional optics, and applying polynomial functors to dynamic systems. This skill is essential for researchers and engineers looking to move beyond black-box models toward mathematically sound, modular, and compositionally robust AI architectures.

Key Features

Accesses a curated knowledge base of category theory lectures and research papers.
Implements categorical optics and lenses for formal bidirectional gradient flow modeling.
2 GitHub stars
Incorporates concepts from geometric deep learning, equivariance, and natural transformations.
Provides structural guidance for polynomial functors (Poly) and parametric morphisms (Para).
Utilizes GF(3) trit-based logic for balanced system state and color saturation mapping.

Use Cases

Formalizing AI system semantics and epistemic states using Kripke frames and inaccessible worlds.
Designing modular neural network architectures using categorical compositionality principles.
Implementing custom backpropagation and optimization logic based on lens theory.

About

Key Features

Accesses a curated knowledge base of category theory lectures and research papers.
Implements categorical optics and lenses for formal bidirectional gradient flow modeling.
2 GitHub stars
Incorporates concepts from geometric deep learning, equivariance, and natural transformations.
Provides structural guidance for polynomial functors (Poly) and parametric morphisms (Para).
Utilizes GF(3) trit-based logic for balanced system state and color saturation mapping.

Use Cases

Formalizing AI system semantics and epistemic states using Kripke frames and inaccessible worlds.
Designing modular neural network architectures using categorical compositionality principles.
Implementing custom backpropagation and optimization logic based on lens theory.