Models and analyzes time-varying data using sheaves on time categories for compositional temporal reasoning.
Bumpus Narratives is a highly specialized skill designed for advanced temporal data modeling and analysis within the Claude Code environment. Based on the categorical research of B.M. Bumpus, it implements sheaves on time categories to facilitate structured reasoning about data that evolves across intervals. The skill utilizes sophisticated mathematical techniques such as the Adhesion Filter (FPT algorithm) for efficient tree decompositions and Cech cohomology to detect local-to-global consistency obstructions. It is an essential tool for developers and data scientists working with complex, time-dependent systems that require formal verification and compositional coherence.
주요 기능
01Sheaf-based modeling for structured temporal data intervals
02Cohomological obstruction detection via H⁰ analysis
032 GitHub stars
04Deterministic narrative visualization with Gay.jl color chains
05Adhesion Filter (FPT) for efficient data consistency checking
06Triadic composition support for GF(3) conservation and balance
사용 사례
01Verifying consistency across distributed time-varying datasets
02Modeling organizational transitions like mergers, acquisitions, and supply chain evolutions
03Implementing compositional algorithms on complex tree-structured decompositions
