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ix-topo

Analyze 3D point cloud data via persistent homology computations.

Updated Mar 12, 2026
Search Tags:#topology#point cloud#topological analysis#persistence homology#3D data
One-click install
npx skills add https://github.com/GuitarAlchemist/ix --skill ix-topo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ix-topo
Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/ix-topo
Command: npx skills add https://github.com/GuitarAlchemist/ix --skill ix-topo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The ix-topo Skill unit addresses the challenge of performing complex topological data analysis on point cloud data.

Core Features & Use Cases

  • Persistent Homology: Detects birth-death pairs across filtration radii, essential for understanding the shape and connectivity of data.
  • Betti Numbers: Calculates the Betti numbers for connected components, loops, and voids at specific scales, indicating topological changes.
  • Topological Features: Assists users in visualizing the underlying topological properties of data points.
  • Use Case: Users who work with 3D point cloud data can utilize ix-topo to study the topology of shapes or to extract features for machine learning or other data science tasks.

Quick Start

Calculate the persistence homology of point cloud data from file 'example_cloud.pcl'.

Frequently Asked Questions about ix-topo

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

FAQPage Schema
How do I calculate persistent homology for 3D point cloud data?▼

To calculate persistent homology for 3D point cloud data, use specialized algorithms that detect birth-death pairs across filtration radii. This process identifies shape properties and connectivity information essential for geometric analysis and 3D modeling.

What are Betti numbers and how do they indicate topological changes in point clouds?▼

Betti numbers in topological analysis indicate connected components, loops, and voids at specific scales within point clouds. They reveal topological changes in the data by quantifying these structural features across different filtration radii.

Can I extract topological features from point clouds for machine learning?▼

Yes, you can extract topological features from point cloud data for machine learning. By computing persistent homology and Betti numbers, you transform spatial and connectivity information into structured features suitable for data science tasks.

Does point cloud topological analysis work for identifying shape properties in 3D modeling?▼

Point cloud topological analysis works for identifying shape properties in 3D modeling by evaluating spatial and connectivity information. It calculates persistent homology to reveal underlying structural features like loops and voids within the geometric data.

What is the best way to visualize topological properties of 3D data points?▼

The best way to visualize topological properties of 3D data points is by computing persistent homology to map birth-death pairs. This reveals the underlying shape and connectivity of the data across varying filtration scales.

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