What is the purpose of the sonification feature?

Sonification maps surprisal values to pitch classes. This allows developers to 'hear' the health of their AI operations in the background, where dissonant chords signify high error rates or unexpected system behaviors.

How do the three attention channels help in analysis?

The Direct channel looks for exact matches, Diffuse looks for thematic or structural similarities, and Meta tracks changes in the system's underlying capabilities, providing a comprehensive 360-degree evaluation.

Do I need a background in Category Theory to use this skill?

While the skill is built on advanced mathematical principles like Cat# and Galois adjunctions, it provides automated CLI outputs and clear metrics that can be used by any developer without deep theoretical knowledge.

What is behavior surprisal in this context?

Surprisal is a mathematical measure of how unexpected an observed outcome is based on a prior prediction. High surprisal indicates a significant deviation in the AI's logic or understanding of the task environment.

Behavior Surprisal Analysis

Name: Behavior Surprisal Analysis
Author: plurigrid

byplurigrid

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Analytics & Monitoring

Evaluates the accuracy and information-theoretic entropy of AI predictions using advanced category theory and belief revision frameworks.

About

Behavior Surprisal Analysis is a sophisticated auditing tool for Claude Code that measures 'surprisal'—the mathematical unexpectedness of an outcome relative to a prediction. By leveraging Cat# (Category Theory) structures and AGM belief revision, it analyzes outcomes across three channels: Direct artifact matching, Diffuse thematic consistency, and Meta capability tracking. This skill is essential for developers building high-reliability AI systems who need to quantify model drift, verify logical coherence via Galois adjunctions, and even monitor system states through pitch-based sonification.

Key Features

Cat# Integration using Galois adjunctions to ensure mathematical coherence between abstract and concrete results.
Real-time sonification of surprisal values using the CatSharp music theory scale for auditory monitoring.
2 GitHub stars
Tri-channel prediction evaluation (Direct, Diffuse, and Meta) for holistic model auditing.
AGM Belief Revision via the Levi Identity to programmatically update models based on observed contradictions.
GF(3) triadic logic conservation to maintain balanced entropy and naturality across system states.

Use Cases

Quantifying the divergence between autonomous AI plans and actual execution results in complex workflows.
Monitoring LLM capability drift and 'hallucination' rates during long-term project development.
Using auditory feedback to identify high-entropy events in background automated testing suites.

About

Key Features

Cat# Integration using Galois adjunctions to ensure mathematical coherence between abstract and concrete results.
Real-time sonification of surprisal values using the CatSharp music theory scale for auditory monitoring.
2 GitHub stars
Tri-channel prediction evaluation (Direct, Diffuse, and Meta) for holistic model auditing.
AGM Belief Revision via the Levi Identity to programmatically update models based on observed contradictions.
GF(3) triadic logic conservation to maintain balanced entropy and naturality across system states.

Use Cases

Quantifying the divergence between autonomous AI plans and actual execution results in complex workflows.
Monitoring LLM capability drift and 'hallucination' rates during long-term project development.
Using auditory feedback to identify high-entropy events in background automated testing suites.