How does the determinism work in SplitMixTernary?

It uses a fixed seed combined with the hash of a proposition through the SplitMix64 algorithm, ensuring the same input always yields the same result eternally.

What is a 'trit' in the context of this skill?

A trit is a ternary digit representing three possible states: +1 (Affirm/Acceleration), 0 (Suspend/Ergodic), or -1 (Negate/Deterritorialization).

What is the practical application of Hyperrealpolitik?

It is used for modeling simulations where political decisions are treated as functions of hash collisions in deterministic RNG streams rather than subjective human intervention.

Does this skill require external API calls for opinion generation?

No, the opinion formation is entirely algorithmic and local, relying on mathematical properties of the SplitMix64 generator and GF(3) arithmetic.

Which programming languages are supported?

The skill provides implementations for over 18 languages, including Python, Julia, Clojure, Rust, Go, TypeScript, and Lean 4.

SplitMixTernary Opine

Name: SplitMixTernary Opine
Author: plurigrid

byplurigrid

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Data Science & ML

Generates deterministic, triadic opinions on propositions using a SplitMix64-based hashing algorithm for algorithmic hyperrealpolitik.

About

SplitMixTernary Opine is a specialized Claude Code Skill that implements deterministic opinion formation through GF(3) coloring and the SplitMix64 random number generator. By combining a fixed seed with a text proposition, it produces a consistent 'trit' output—Affirm (+1), Suspend (0), or Negate (-1)—ensuring that the same inputs always result in the same computational 'opinion.' This tool is designed for developers and researchers exploring algorithmic philosophy, hyperreal simulations, and the intersection of political repetition and deterministic hashing across multiple programming languages including Python, Julia, and Clojure.

Key Features

Cross-language support for Python, Julia, Clojure, and 15+ other languages
Algorithmic framework for hyperrealpolitik and simulation modeling
Implementation of SplitMix64 for high-quality hash distribution
GF(3) conservation logic to ensure balanced triadic groupings
Deterministic triadic opinion generation (Affirm, Suspend, Negate)
2 GitHub stars

Use Cases

Modeling deterministic decision-making systems in philosophical simulations
Generating reproducible sentiment tags for experimental data labeling
Creating consistent NPC viewpoints or procedural personalities in game development

About

Key Features

Cross-language support for Python, Julia, Clojure, and 15+ other languages
Algorithmic framework for hyperrealpolitik and simulation modeling
Implementation of SplitMix64 for high-quality hash distribution
GF(3) conservation logic to ensure balanced triadic groupings
Deterministic triadic opinion generation (Affirm, Suspend, Negate)
2 GitHub stars

Use Cases

Modeling deterministic decision-making systems in philosophical simulations
Generating reproducible sentiment tags for experimental data labeling
Creating consistent NPC viewpoints or procedural personalities in game development