prime-radiant-advanced-wasm
CommunityAI interpretability via advanced math.
Software Engineering#category theory#webassembly#causal inference#spectral analysis#ai interpretability#homotopy type theory
Authorricable
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a framework for understanding and auditing neural network behavior using advanced mathematical and theoretical concepts, making complex AI models more transparent and trustworthy.
Core Features & Use Cases
- Mathematical AI Interpretability: Leverages Category Theory, Homotopy Type Theory, Spectral Analysis, Causal Inference, Quantum Topology, and Sheaf Cohomology.
- WebAssembly Compilation: Enables high-performance execution directly in browsers or Node.js environments.
- Use Case: Analyze the spectral properties of a neural network's layers to identify potential biases or vulnerabilities, or perform causal inference on AI decisions to understand their reasoning.
Quick Start
Use the prime-radiant-advanced-wasm skill to analyze model weights with spectral and causal modules.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: prime-radiant-advanced-wasm Download link: https://github.com/ricable/cli-skills-builder/archive/main.zip#prime-radiant-advanced-wasm Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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