Analyzes conversation threads and concept networks using relational ACSet patterns and HyJAX structures.
This skill empowers Claude to transform unstructured conversation history into structured relational databases using Category Theory principles like ACSets. It extracts hub concepts, identifies multi-hop relationship paths between ideas, and generates colored S-expressions for visual representation. By leveraging DuckDB for persistence and HyJAX for pattern matching, it allows users to perform sophisticated semantic analysis on complex dialogue threads, calculating metrics like concept entropy and information gain to optimize knowledge extraction and thread mapping.
Key Features
01Colored S-expression generation for hierarchical data visualization
02Automated concept extraction and hub-score analysis via DuckDB
03Multi-hop concept path discovery and relationship weight tracking
04Information entropy and efficiency metrics for thread content analysis
058 GitHub stars
06ACSet-based relational mapping for threads, messages, and concepts
Use Cases
01Identifying central themes and hub concepts across multiple communication channels
02Analyzing information density and concept distribution in technical documentation
03Building a persistent knowledge graph from long AI-human chat histories
