Claude-skill-registry datalog-fixpoint

Datalog bottom-up fixpoint iteration for recursive queries

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/datalog-fixpoint" ~/.claude/skills/majiayu000-claude-skill-registry-datalog-fixpoint && rm -rf "$T"

manifest: skills/data/datalog-fixpoint/SKILL.md

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