Can this skill be used for parallel processing?

Yes. The underlying SplitMixTernary implementation allows you to split generators into independent child generators that still maintain deterministic output, making it ideal for high-performance parallel computation.

What is deterministic color generation?

It is a method where a specific seed value always produces the exact same sequence of colors. This ensures that colors generated in one environment or process will perfectly match those in another, provided the seed is the same.

What are the primary tools included in the MCP server?

The server includes tools like 'color_at' for specific index retrieval, 'palette' for batch generation, and 'golden_thread' for generating colors based on golden angle spirals.

How does the GF(3) trit mapping work?

The skill maps specific hue ranges to three mathematical states: +1 (Plus/Warm), 0 (Ergodic/Neutral), and -1 (Minus/Cold). This allows developers to treat colors as logical values in a ternary system.

Deterministic Color Engine (Gay-MCP)

Name: Deterministic Color Engine (Gay-MCP)
Author: plurigrid

byplurigrid

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Design Tools

Generates mathematically deterministic color palettes and GF(3) trit mappings for reproducible design systems.

Gay-MCP is a specialized skill for Claude Code that enables deterministic color generation using the SplitMix64 algorithm and golden angle principles. It ensures that a specific seed always produces an identical sequence of colors, making it an essential tool for parallel computing, UI consistency, and reproducible design workflows. By mapping hues to Galois Field 3 (GF(3)) trits—categorized as Plus (warm), Ergodic (neutral), and Minus (cold)—it provides a unique mathematical framework for deriving visual state from numeric seeds without temporal dependency.

Key Features

01Deterministic SplitMix64 color generation for guaranteed reproducibility

022 GitHub stars

03Golden angle spiral algorithms for naturally balanced palettes

04GF(3) trit mapping to categorize hues into ternary logic states

05Parallel-ready generator splitting for multi-threaded environments

06Out-of-order mathematical proof ensuring consistency across shuffled indices

Use Cases

01Developing reproducible design systems that maintain consistency across different platforms and seeds.

02Mapping complex application states or data points to specific, predictable color trilogies.

03Generating mathematically optimal color palettes for data visualization and generative art.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi gay-mcp

For use in Claude.ai and ChatGPT

Download Skill