How does it improve multi-agent systems?

It utilizes a GF(3) triad system (Generator, Coordinator, Validator) to optimize joint information gain and reach Nash equilibrium in collaborative tasks.

Does it integrate with data tools like DuckDB?

Yes, it includes SQL patterns for storing, querying, and exporting optimized sequences within DuckDB environments.

What is temperature-aware sequencing?

It uses Langevin dynamics to control the trade-off between exploring new patterns (high temperature) and exploiting known high-information patterns (low temperature).

What is the theoretical basis of this skill?

It is based on Active Inference, Soatto's Actionable Information Framework, and Compositional Game Theory.

What does the Entropy Sequencer do?

It reorders interaction sequences to maximize information gain (epistemic value), allowing models and agents to learn patterns significantly faster than chronological processing.

Entropy Sequencer

Name: Entropy Sequencer
Author: plurigrid

byplurigrid

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データサイエンスとML

Optimizes interaction sequences using information theory to maximize learning efficiency and minimize surprise.

概要

The Entropy Sequencer is a specialized Claude Code skill that leverages Active Inference and information-theoretic foundations to reorder interactions for maximum epistemic value. By interleaving interactions based on Soatto's actionable information framework and Langevin dynamics, it enables agents to learn patterns up to three times faster than chronological processing. It is particularly useful for complex problem-solving where the order of information intake significantly impacts the discovery of latent structures, the reduction of uncertainty, and the efficiency of multi-agent coordination.

主な機能

Nuisance factorization to isolate minimal sufficient statistics from noisy data
Maximum information gain reordering via greedy entropy-based arranging
Compositional strategy optimization using Open Games theory and Nash equilibrium
2 GitHub stars
Temperature-aware sequencing using Langevin dynamics for exploration-exploitation balance
Predictability scoring to measure autocorrelation and temporal monotonicity in sequences

ユースケース

Optimizing multi-agent interaction flows to identify high-impact communication strategies
Accelerating pattern recognition in complex datasets for faster machine learning model training
Detecting anomalous domain generation algorithms (DGA) through entropy signature analysis

概要

主な機能

Nuisance factorization to isolate minimal sufficient statistics from noisy data
Maximum information gain reordering via greedy entropy-based arranging
Compositional strategy optimization using Open Games theory and Nash equilibrium
2 GitHub stars
Temperature-aware sequencing using Langevin dynamics for exploration-exploitation balance
Predictability scoring to measure autocorrelation and temporal monotonicity in sequences

ユースケース

Optimizing multi-agent interaction flows to identify high-impact communication strategies
Accelerating pattern recognition in complex datasets for faster machine learning model training
Detecting anomalous domain generation algorithms (DGA) through entropy signature analysis