What is the benefit of the Cat# integration?

The Cat# integration maps fixpoint computation to coalgebraic semantics, providing a formal framework for understanding convergence as reaching a terminal coalgebra via natural transformations.

Does this skill support high-performance data processing?

Yes, it is designed to interleave with the Polars ecosystem, allowing the fixpoint iteration to occur efficiently over large dataframes.

What is a Datalog fixpoint in this context?

A fixpoint in Datalog is the mathematical state reached when further applications of inference rules produce no new data, signifying that a recursive query has been fully evaluated.

How does this skill handle recursion without explicit syntax?

It utilizes bottom-up saturation, which iteratively applies logic rules to existing facts until no more facts can be derived, avoiding the overhead of traditional recursive function calls.

Datalog Fixpoint Iteration

Name: Datalog Fixpoint Iteration
Author: plurigrid

byplurigrid

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データサイエンスとML

Implements bottom-up fixpoint iteration for recursive Datalog queries using saturation-based evaluation.

概要

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.

主な機能

High-performance integration with Polars dataframes
Bottom-up fixpoint iteration using immediate consequence operators
Saturation-based evaluation for complex recursive queries
Coalgebraic semantics for formal convergence guarantees
Support for ERGODIC iterative bridges and terminal coalgebras
2 GitHub stars

ユースケース

Transitive closure calculations for large-scale graph analysis
Static program analysis and reachability testing in source code
Symbolic reasoning engines for AI-driven knowledge graphs

概要

主な機能

High-performance integration with Polars dataframes
Bottom-up fixpoint iteration using immediate consequence operators
Saturation-based evaluation for complex recursive queries
Coalgebraic semantics for formal convergence guarantees
Support for ERGODIC iterative bridges and terminal coalgebras
2 GitHub stars

ユースケース

Transitive closure calculations for large-scale graph analysis
Static program analysis and reachability testing in source code
Symbolic reasoning engines for AI-driven knowledge graphs