What is the primary benefit of Gay-MCP?

It provides mathematical certainty that a specific seed will always produce the same color sequence, enabling perfect reproducibility across different machines and sessions.

Is it compatible with web development?

Absolutely, the skill includes built-in conversions from the perceptually uniform OkLCH space to standard Hex codes for CSS and frontend styling.

How does the trit mapping work?

Hues are mapped to a Galois Field (GF(3)), categorizing colors into Plus (+1/warm), Ergodic (0/neutral), or Minus (-1/cold) states based on their degree in the color wheel.

Can I use this for machine learning?

Yes, it integrates with discrete backpropagation and Langevin dynamics to track how specific colors (representing noise or state) influence parameter updates.

Gay-MCP Deterministic Colors

Name: Gay-MCP Deterministic Colors
Author: plurigrid

byplurigrid

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设计工具

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

关于

Gay-MCP is a specialized skill for deterministic color generation using the SplitMix64 algorithm and the golden angle. It ensures that the same seed always produces the same visual output, making it ideal for parallel computation, audit trails in machine learning, and cross-platform UI consistency. By mapping colors to GF(3) trits ({-1, 0, +1}), it bridges the gap between aesthetic choices and discrete mathematical logic, allowing for gradient-free optimization and topological analysis of data and codebases.

主要功能

Cross-language support for Julia, Ruby, and Python
High-fidelity OkLCH to Hex color space conversion
Deterministic color generation via SplitMix64 and golden angle
Parallel-safe generator splitting for distributed systems
2 GitHub stars
Automatic mapping of hues to GF(3) trits (Plus, Ergodic, Minus)

使用场景

Creating consistent UI skinning across distributed applications
Visualizing audit trails in Langevin dynamics and ML training
Generating reproducible function fingerprints for code analysis

关于

主要功能

Cross-language support for Julia, Ruby, and Python
High-fidelity OkLCH to Hex color space conversion
Deterministic color generation via SplitMix64 and golden angle
Parallel-safe generator splitting for distributed systems
2 GitHub stars
Automatic mapping of hues to GF(3) trits (Plus, Ergodic, Minus)

使用场景

Creating consistent UI skinning across distributed applications
Visualizing audit trails in Langevin dynamics and ML training
Generating reproducible function fingerprints for code analysis