Which clustering algorithms are supported by this skill?

The skill supports a variety of popular unsupervised learning algorithms including K-means, DBSCAN, and hierarchical clustering, typically implemented via the scikit-learn library.

Does this skill handle data preprocessing?

Yes, it automatically includes essential preprocessing steps such as feature scaling and normalization to ensure that the clustering results are accurate and unbiased.

Can I see a visual representation of the clusters?

Absolutely. The skill generates visual artifacts like scatter plots with cluster labels, making it easy to interpret the groupings and data structure.

Is this skill suitable for anomaly detection?

Yes, specifically when using the DBSCAN algorithm, the skill is highly effective at identifying outliers that do not belong to any dense cluster, which is ideal for anomaly detection.

Clustering Analysis Runner

Name: Clustering Analysis Runner
Author: intent-solutions-io

byintent-solutions-io

0•

Data Science & ML

Executes and visualizes diverse clustering algorithms to identify patterns and groupings within complex datasets automatically.

This skill empowers Claude to automate the end-to-end data science lifecycle for unsupervised learning tasks, ranging from data preprocessing and scaling to the execution of sophisticated algorithms like K-means, DBSCAN, and hierarchical clustering. It is specifically designed to help developers and data scientists quickly uncover hidden structures in data, segment audiences, or detect anomalies without the need to write repetitive boilerplate machine learning code. By providing built-in performance metrics and visualization capabilities, it transforms raw data into actionable insights directly within the Claude Code environment.

Key Features

01Automated data preprocessing including scaling and normalization

02Support for K-means, DBSCAN, and hierarchical clustering algorithms

03Generation of performance metrics like Silhouette and Davies-Bouldin indices

04Integrated visualization of clusters and outliers via scatter plots

05Robust error handling and data validation for ML pipelines

060 GitHub stars

Use Cases

01Analyzing high-dimensional research data to discover natural groupings and structures

02Identifying anomalous patterns in network traffic or financial transactions for security

03Segmenting customer databases into distinct behavioral groups for targeted marketing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add intent-solutions-io/plugins-nixtla skill-adapter

For use in Claude.ai and ChatGPT

Download Skill