What is the difference between Stratification and Fabrication?

Stratification involves hierarchical layering of structures like evidence and goals, while Fabrication focuses on the compositional assembly of components into a unified whole.

Who should use the Kinetic Block skill?

This skill is designed for advanced developers and researchers building complex, autonomous systems that require rigorous mathematical frameworks for behavior management.

What is a Kinetic Block in Claude Code?

A Kinetic Block is the atomic unit of ASI skill orchestration, consisting of three operations: stratification (layering), fabrication (composition), and conservation (verification).

How does GF(3) conservation ensure system stability?

It uses trit assignments (-1, 0, +1) across the three block operations to ensure the sum remains 0 mod 3, providing a mathematical invariant for system integrity.

Kinetic Block Orchestrator

Name: Kinetic Block Orchestrator
Author: plurigrid

byplurigrid

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Data Science & ML

Orchestrates complex AI behaviors using a tripartite framework of hierarchical stratification, compositional fabrication, and mathematical conservation.

About

The Kinetic Block Skill is a sophisticated orchestration framework designed for building autonomous, self-verifying AI systems within the ASI (Artificial Super Intelligence) paradigm. It conceptualizes software tasks as 'kinetic blocks'—topological units that balance hierarchical layering (stratification), component assembly (fabrication), and invariant verification (conservation) using GF(3) mathematical logic. This skill is particularly useful for developers working with category theory, operad algebras, and active inference models, providing a rigorous structure for managing information energy and structural entropy in complex codebases.

Key Features

Multi-language support with native interfaces for both Python and Julia
Compositional Fabrication using operad algebras and colimit assembly patterns
GF(3) Conservation Logic to ensure system stability and invariant verification
Information Energy Framework for tracking processing velocity and structural depth
Hierarchical Stratification for separating passive evidence from active emergent goals
2 GitHub stars

Use Cases

Architecting self-modifying autonomous agents and multi-agent orchestration layers
Implementing verifiable hierarchical control systems for complex distributed architectures
Modeling information flow and structural stability in large-scale topological systems

About

Key Features

Multi-language support with native interfaces for both Python and Julia
Compositional Fabrication using operad algebras and colimit assembly patterns
GF(3) Conservation Logic to ensure system stability and invariant verification
Information Energy Framework for tracking processing velocity and structural depth
Hierarchical Stratification for separating passive evidence from active emergent goals
2 GitHub stars

Use Cases

Architecting self-modifying autonomous agents and multi-agent orchestration layers
Implementing verifiable hierarchical control systems for complex distributed architectures
Modeling information flow and structural stability in large-scale topological systems