Implements bottom-up fixpoint iteration for recursive Datalog queries using saturation-based evaluation.
This skill provides Claude with the specialized ability to perform complex recursive query evaluations using Datalog's bottom-up fixpoint iteration. It leverages iterative saturation to reach convergence without requiring explicit recursive syntax, making it ideal for graph processing, program analysis, and symbolic reasoning. By integrating with high-performance Polars dataframes and applying coalgebraic semantics through Cat# principles, this skill ensures robust, mathematically sound convergence for topological data processing.
Key Features
01High-performance integration with Polars dataframes
02Bottom-up fixpoint iteration using immediate consequence operators
03Saturation-based evaluation for complex recursive queries
04Coalgebraic semantics for formal convergence guarantees
05Support for ERGODIC iterative bridges and terminal coalgebras
062 GitHub stars
Use Cases
01Transitive closure calculations for large-scale graph analysis
02Static program analysis and reachability testing in source code
03Symbolic reasoning engines for AI-driven knowledge graphs
