What programming languages are supported by this MCP server?

The Gay-MCP skill provides direct integration for Julia through the Gay.jl package and Ruby via the SplitMixTernary library.

Can this skill be used for parallel processing?

Yes, it supports generator splitting, allowing you to create independent child generators that produce predictable results in parallel without inter-process communication.

How are colors mapped to GF(3) trits?

Colors are categorized by their hue values: warm hues (0-60°, 300-360°) map to +1, neutral hues (60-180°) map to 0, and cold hues (180-300°) map to -1.

What makes Gay-MCP color generation deterministic?

Gay-MCP uses the SplitMix64 PRNG algorithm with a fixed seed and the golden angle, ensuring that the same seed and index always yield the exact same color output across different environments.

Gay-MCP Deterministic Color System

Name: Gay-MCP Deterministic Color System
Author: plurigrid

byplurigrid

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Design Tools

Generates mathematically deterministic color palettes and GF(3) trits using SplitMix64 for perfectly reproducible design systems.

About

Gay-MCP is a specialized tool for developers needing rigorous, reproducible color generation across distributed or parallel systems. By leveraging the SplitMix64 algorithm and the golden angle, it ensures that any given seed produces the exact same sequence of colors and trits (-1, 0, +1) regardless of the execution environment. This skill integrates Gay.jl (Julia) and SplitMixTernary (Ruby) implementations, allowing Claude to manage complex color palettes, perform out-of-order mathematical proofs, and maintain state-consistent visual identities in advanced computational design workflows.

Key Features

Deterministic color generation via SplitMix64 and golden angle algorithms
Parallel-ready seed splitting to create independent yet consistent child generators
Cross-platform support via Julia (Gay.jl) and Ruby (SplitMixTernary) implementations
2 GitHub stars
GF(3) trit mapping for categorizing colors into warm (+1), neutral (0), and cold (-1) states
Mathematical out-of-order proofs to verify consistency across shuffled or reversed indices

Use Cases

Mapping complex mathematical states to deterministic color values in data visualization
Developing reproducible design systems where a single seed serves as the source of truth
Generating consistent visual identities across decentralized or parallel rendering nodes

About

Key Features

Deterministic color generation via SplitMix64 and golden angle algorithms
Parallel-ready seed splitting to create independent yet consistent child generators
Cross-platform support via Julia (Gay.jl) and Ruby (SplitMixTernary) implementations
2 GitHub stars
GF(3) trit mapping for categorizing colors into warm (+1), neutral (0), and cold (-1) states
Mathematical out-of-order proofs to verify consistency across shuffled or reversed indices

Use Cases

Mapping complex mathematical states to deterministic color values in data visualization
Developing reproducible design systems where a single seed serves as the source of truth
Generating consistent visual identities across decentralized or parallel rendering nodes