Can this skill be used for AI feedback loop verification?

Yes, it is designed to verify 'LoopClosed' states where predictions and observations match exactly over multiple iterations, ensuring the identity of the generator and observer.

What is the purpose of the Criticality Detector Skill?

It identifies whether a dynamical system is in a state of chaos, criticality, or order by measuring error against fixed-point attractors using a specific seed-based observer model.

What are the required dependencies for this skill?

This skill integrates with the Gay.jl MCP and is compatible with the K-Dense-AI scientific skills ecosystem, including Graph Theory and Koopman Generator skills.

How does it classify system phases?

Phase classification is determined by error bounds: Chaos (error > 0.5), Critical (error ≈ 0.1), and Ordered (error < 0.01), each mapped to specific visual hues.

What is the 'Golden Thread' in this context?

The Golden Thread is a φ-derived (golden ratio) hue spiral used to visualize system states and facilitate perceptual control via color-based comparators within the MCP integration.

Criticality Detector

Name: Criticality Detector
Author: plurigrid

byplurigrid

•

데이터 과학 및 ML

Detects phase transitions and classifies dynamical system states using fixed-point distance measurements and self-loop closure verification.

소개

The Criticality Detector Skill provides specialized tools for analyzing complex dynamical systems by measuring the distance to fixed points through comparator error analysis. It utilizes a golden ratio-derived hue spiral (the Golden Thread) to visualize and classify system phases into Chaos, Critical, or Ordered states. By integrating with the Gay.jl MCP, this skill enables self-loop closure detection and perceptual control validation, making it an essential utility for developers working on complex AI feedback loops, cybernetic systems, or topological models where monitoring stability and bifurcation points is critical.

주요 기능

2 GitHub stars
Golden Thread visualization using φ-derived hue spirals
GF(3) triadic balance conservation for compositional coherence
Fixed-point distance measurement via comparator error calculation
Self-loop closure detection for generator and observer identity verification
Phase classification across Chaos, Critical, and Ordered regimes

사용 사례

Monitoring stability and bifurcation in complex AI-driven feedback loops
Visualizing system state transitions using color-mapped perceptual control
Validating self-referential identity in topological and cybernetic computing models

소개

주요 기능

2 GitHub stars
Golden Thread visualization using φ-derived hue spirals
GF(3) triadic balance conservation for compositional coherence
Fixed-point distance measurement via comparator error calculation
Self-loop closure detection for generator and observer identity verification
Phase classification across Chaos, Critical, and Ordered regimes

사용 사례

Monitoring stability and bifurcation in complex AI-driven feedback loops
Visualizing system state transitions using color-mapped perceptual control
Validating self-referential identity in topological and cybernetic computing models