Skip to main content
AI/MLplurigrid

datalog-fixpoint

Datalog bottom-up fixpoint iteration for recursive queries

Stars
23
Source
plurigrid/asi
Updated
2026-04-26
Slug
plurigrid--asi--datalog-fixpoint
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/plurigrid/asi/HEAD/plugins/asi/skills/datalog-fixpoint/SKILL.md -o .claude/skills/datalog-fixpoint.md

Drops the SKILL.md into .claude/skills/datalog-fixpoint.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

Datalog Fixpoint Skill

Bottom-up fixpoint iteration for recursive Datalog queries without explicit recursion.

Core Concept

Datalog computes fixpoints via iterative saturation:

T^0(∅) → T^1 → T^2 → ... → T^ω (fixpoint)

Where T is the immediate consequence operator.

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Dataframes

  • polars [○] via bicomodule
    • High-performance dataframes

Bibliography References

  • algorithms: 19 citations in bib.duckdb

Cat# Integration

Fixpoint computation maps to Cat# via coalgebraic semantics:

Trit: 0 (ERGODIC - iterative bridge)
Home: Prof (profunctors/bimodules)
Poly Op: ⊗ (parallel saturation)
Kan Role: Adj (Kleisli adjunction)

GF(3) Naturality

Datalog fixpoint iteration is inherently ERGODIC:

  • Each iteration step is a natural transformation
  • Convergence = reaching the terminal coalgebra
  • The fixpoint IS the bicomodule equilibrium