How does this skill measure AI intelligence?

Based on the thesis that 'compression is understanding,' this skill evaluates intelligence by measuring how effectively a model can find shorter descriptions or programs for complex datasets.

Is Kolmogorov complexity actually computable?

In a strict mathematical sense, it is uncomputable due to the halting problem. However, this skill uses LLMs and heuristic search to provide high-quality approximations known as the KoLMogorov-Test.

What technologies does this skill use?

The skill utilizes Python for general implementation, MLX for machine learning efficiency, and Lean4 for formal verification of theorems and logic.

What is Kolmogorov Complexity?

Kolmogorov Complexity is the length of the shortest possible program required to generate a specific piece of data. It serves as a mathematical measure of the information content and complexity of that data.

Kolmogorov Compression

Name: Kolmogorov Compression
Author: plurigrid

byplurigrid

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Data Science & ML

Measures and optimizes data complexity by finding the shortest algorithmic representation to evaluate intelligence and information density.

The Kolmogorov Compression skill enables Claude to apply algorithmic information theory to code and data structures. By approximating the shortest program that can reproduce a given dataset (K-complexity), it provides a rigorous framework for evaluating model intelligence, validating information density, and filtering noise. It leverages modern LLM-based testing (KoLMogorov-Test 2025) to bridge the gap between abstract Turing machine theory and practical Python/MLX implementations, making it an essential tool for advanced AI research and high-efficiency code optimization.

Key Features

01Implements the KoLMogorov-Test (2025) for intelligence benchmarking

02Analyzes information density and redundancy in codebases

03Provides domain-specific validation using GF(3) triad logic

04Estimates Kolmogorov Complexity K(x) via LLM-driven program synthesis

057 GitHub stars

06Integrates with Lean4 for formal verification of algorithmic simplicity

Use Cases

01Benchmarking AI model intelligence through compression-to-data ratios

02Minimizing boilerplate code by finding the most concise algorithmic representation

03Identifying latent patterns and structural depth within complex datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi kolmogorov-compression

For use in Claude.ai and ChatGPT

Download Skill