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ruvector-math-wasm-scoped

Compute Wasserstein distances and Fisher metrics via scoped WebAssembly packages.

1|Updated Feb 8, 2026
Search Tags:#typescript#mathematics#wasm#webassembly#information geometry#optimal transport
One-click install
npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-math-wasm-scoped
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ruvector-math-wasm-scoped
Source: https://github.com/ricable/cli-skills-builder/tree/main/.claude/skills/ruvector-math-wasm-scoped
Command: npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-math-wasm-scoped

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides efficient, scoped WebAssembly implementations for advanced mathematical operations like Optimal Transport and Information Geometry, enabling complex calculations in browser or Node.js environments.

Core Features & Use Cases

  • Optimal Transport: Compute Wasserstein distances and use Sinkhorn solvers for cost-minimizing transport plans.
  • Information Geometry: Calculate Fisher information metrics and natural gradients for statistical manifolds.
  • Product Manifolds: Perform operations like geodesics, exponential, and logarithmic maps on product manifolds.
  • Use Case: Analyze the divergence between two probability distributions using Wasserstein distance or compute the curvature of a statistical model using the Fisher metric.

Quick Start

Import the WassersteinDistance class and compute the distance between two probability distributions.

Frequently Asked Questions about ruvector-math-wasm-scoped

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute Wasserstein distances in a browser or Node.js environment?▼

To compute Wasserstein distances in a browser or Node.js environment, use this Skill's scoped WebAssembly packages and JavaScript bindings to calculate Optimal Transport plans efficiently without external math servers.

What's the best way to calculate Fisher information metrics for statistical manifolds in JavaScript?▼

The best way to calculate Fisher information metrics in JavaScript is using WebAssembly implementations of Information Geometry, which compute natural gradients and statistical manifold curvature directly in your runtime environment.

Do I need a WebAssembly runtime to perform Optimal Transport calculations in TypeScript?▼

Yes, you need a WebAssembly runtime and JavaScript bindings to execute these scoped WASM packages for Optimal Transport and Product Manifold operations within your TypeScript application.

Can I solve Sinkhorn optimal transport problems using WebAssembly?▼

Yes, you can solve Sinkhorn optimal transport problems using WebAssembly to generate cost-minimizing transport plans, leveraging scoped WASM packages for high-performance numerical computation in web environments.

How do I perform geodesic and exponential maps on product manifolds in JavaScript?▼

You perform geodesic and exponential maps on product manifolds in JavaScript by importing the scoped WebAssembly primitives, which execute complex manifold operations locally in browser or Node.js contexts.

What are the limitations of using WebAssembly for Information Geometry computations?▼

Limitations of using WebAssembly for Information Geometry include requiring specific WASM runtime support and JavaScript bindings for mathematical primitives, ensuring your target environment can execute scoped WASM packages efficiently.

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