gay-mcp

Deterministic color generation uses seeded algorithms like SplitMix64 to guarantee the same seed produces identical colors every time, across threads and languages. This Skill applies golden-angle coloring and GF(3) trits to create reproducible palettes for parallel rendering and cross-platform consistency.

Use seed-driven APIs to generate palettes: pass a seed to the palette or color_at function, which applies SplitMix64 hashing and golden-angle mapping to produce consistent colors. The golden_thread API splits seeds across parallel tasks while maintaining reproducibility.

Yes. Seeded color generation ensures that parallel workers accessing the same seed index produce identical colors without synchronization. This Skill exposes APIs designed for thread-safe, deterministic lookups across Ruby, Julia, Python, and Clojure.

Golden-angle coloring maps seed values to hues using the golden ratio; GF(3) trits add structure for deterministic palette indexing. Combined with SplitMix64, this ensures each seed maps to a unique, reproducible color across runs and implementations.

Seeded palettes are reproducible: the same seed always yields identical outputs, enabling consistent visuals across runs, platforms, and parallel workers. Random generation loses this guarantee, making debugging and cross-team collaboration harder.

Yes. This Skill provides APIs in Ruby, Julia, Python, and Clojure with identical seed-to-color mappings, so you can generate the same palette across different languages and systems.