What is the significance of the GF(3) trits?

Every color generated by the skill maps to a ternary value (-1, 0, or +1). This allows developers to link visual elements directly to mathematical logic or tripartite streams in complex systems.

What makes Gay-MCP different from standard random color generators?

Gay-MCP is strictly deterministic; it uses the SplitMix64 algorithm and a seed-based approach to ensure that the same input always results in the exact same color output, enabling perfect reproducibility.

Which programming languages is this skill compatible with?

The core logic is implemented and provided with patterns for both Julia (Gay.jl) and Ruby (SplitMixTernary), making it suitable for scientific computing and web development.

Can I use this skill for parallel processing tasks?

Yes, the skill supports 'splitting' generators. You can fork a generator for parallel computation and still maintain deterministic results across different threads or processes.

Gay-MCP Deterministic Color & Trit Generator

Name: Gay-MCP Deterministic Color & Trit Generator
Author: plurigrid

byplurigrid

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设计工具

Generates mathematically deterministic color palettes and ternary logic mappings using SplitMix64 and the golden angle.

Gay-MCP is a specialized tool for developers and designers who require absolute reproducibility in color generation and ternary state management. By leveraging the SplitMix64 algorithm and the golden angle, it ensures that any specific seed consistently produces identical color sequences and GF(3) trits across different environments. This skill is ideal for generative design, branding across distributed systems, and complex mathematical simulations where visual outputs must be directly mapped to ternary logic states (-1, 0, +1).

主要功能

01Direct mapping of hues to GF(3) trits for ternary logic implementation

02Multi-language implementation support for both Julia and Ruby environments

03Tripartite stream support for conserved mathematical states

04Deterministic color generation ensuring the same seed always produces the same output

052 GitHub stars

06Parallel-ready architecture with generator splitting for distributed computation

使用场景

01Creating reproducible visual themes and branding for distributed software applications

02Mapping complex mathematical data states to distinct, identifiable colors for visualization

03Implementing ternary-based logic systems in generative art or chemical computing simulations

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