Can I visualize the relational analysis results?

Yes, the skill generates Colored S-expressions that map concept relationships to deterministic colors, providing a visual representation of the network topology.

What is the role of Shannon entropy in this skill?

It provides a quantitative measure of information density and distribution efficiency, helping users understand the complexity and focus of their data sets.

How does the skill handle unstructured conversation data?

It ingests thread data into a persistent DuckDB 'Thread Lake,' extracts key concepts and morphisms, and calculates connectivity scores to identify core themes and relationships.

What are ACSets in the context of this skill?

ACSets (Attributed C-Sets) are mathematical structures used to model complex relational data, allowing the skill to perform sophisticated querying and pattern matching across conversation threads.

HyJAX Relational Thread Analysis

Name: HyJAX Relational Thread Analysis
Author: plurigrid

byplurigrid

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Data Science & ML

Analyzes conversation threads and concept networks using advanced relational thinking and ACSet modeling.

About

The HyJAX Relational Thinking Skill enables Claude to apply sophisticated mathematical frameworks like Algebraic Databases (ACSets) and C-Sets to thread analysis and concept mapping. By extracting structured patterns from unstructured conversation data, it builds persistent relational databases in DuckDB, calculates information-theoretic metrics like Shannon entropy, and generates Colored S-expressions for complex data visualization. This skill is essential for users needing to navigate high-density information flows, identify hub concepts in large knowledge bases, and maintain compositional coherence in complex AI-driven workflows.

Key Features

High-performance relational querying via unified DuckDB integration
Deterministic visual pattern generation via Colored S-expressions and Cat# integration
Advanced concept network analysis including hub scoring and path discovery
Automated ACSet schema mapping for thread objects, messages, and concepts
Information density metrics using Shannon entropy and efficiency calculations
2 GitHub stars

Use Cases

Measuring and optimizing information distribution across large datasets
Mapping complex knowledge graphs from long-form conversation histories
Identifying central hub concepts and 2-hop relationships in technical documentation

About

Key Features

High-performance relational querying via unified DuckDB integration
Deterministic visual pattern generation via Colored S-expressions and Cat# integration
Advanced concept network analysis including hub scoring and path discovery
Automated ACSet schema mapping for thread objects, messages, and concepts
Information density metrics using Shannon entropy and efficiency calculations
2 GitHub stars

Use Cases

Measuring and optimizing information distribution across large datasets
Mapping complex knowledge graphs from long-form conversation histories
Identifying central hub concepts and 2-hop relationships in technical documentation