What role does Powers PCT play in this skill?

It provides a 5-level hierarchical control framework, allowing the system to set reference goals from high-level programs down to sensory intensities.

What is the primary purpose of the RG Flow ACSet skill?

It models Renormalization Group (RG) flows using categorical structures (ACSets), allowing for the rigorous mathematical analysis of multi-scale systems and fixed points.

How does it handle topological defects?

It uses the XY model configuration to track vortex/antivortex pairs and ensure net topological charge conservation (NetChargeZero) across RG steps.

Is this skill compatible with the Julia programming language?

Yes, it is specifically designed to work with Julia's ACSets ecosystem, providing schemas and morphisms for scientific data modeling.

RG Flow ACSet

Name: RG Flow ACSet
Author: plurigrid

byplurigrid

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Data Science & ML

Implements Renormalization Group (RG) flows using categorical ACSet semantics and topological XY model defect analysis.

About

The RG Flow ACSet skill provides a robust framework for modeling multi-scale systems and topological transitions using Julia-based Attributed C-Sets. By integrating XY model configuration and Powers Perceptual Control Theory (PCT), it enables Claude to simulate complex physical flows, identify fixed points, and maintain conservation laws via GF(3) triadic color mapping. This is particularly useful for researchers and developers working in computational physics, category theory, or hierarchical control systems who need a rigorous way to represent coarse-graining, universality, and phase transitions in complex systems.

Key Features

Categorical mapping of RG flow steps via SchRGFlow schema
2 GitHub stars
Fixed point convergence and universality analysis in Julia
Hierarchical control implementation using 5-level Powers PCT cascade
GF(3) charge conservation through triadic color palette generation
Topological defect tracking for XY model configurations

Use Cases

Designing hierarchical control loops for autonomous physical systems
Automating categorical data migrations and coarse-graining in scientific computing
Modeling critical phenomena and BKT transitions in statistical mechanics

About

Key Features

Categorical mapping of RG flow steps via SchRGFlow schema
2 GitHub stars
Fixed point convergence and universality analysis in Julia
Hierarchical control implementation using 5-level Powers PCT cascade
GF(3) charge conservation through triadic color palette generation
Topological defect tracking for XY model configurations

Use Cases

Designing hierarchical control loops for autonomous physical systems
Automating categorical data migrations and coarse-graining in scientific computing
Modeling critical phenomena and BKT transitions in statistical mechanics