Which algorithms does this skill support?

The skill supports popular clustering methods including K-means, DBSCAN, and hierarchical clustering.

Can it help with anomaly detection?

Yes, it specifically leverages density-based algorithms like DBSCAN to identify outliers and anomalies in your datasets.

Do I need to preprocess my data first?

While recommended, the skill includes automated preprocessing steps like scaling and normalization to ensure optimal clustering performance.

What Python libraries are used for the analysis?

The skill primarily utilizes scikit-learn for machine learning, pandas for data manipulation, and matplotlib or seaborn for visualizations.

Does it provide visualizations?

Yes, the skill generates visual outputs such as scatter plots with cluster labels to help you interpret the groupings visually.

Clustering Algorithm Runner

Name: Clustering Algorithm Runner
Author: jeremylongshore

byjeremylongshore

•

896

•

Data Science & ML

Performs automated data clustering analysis using K-means, DBSCAN, and hierarchical algorithms to identify patterns and segments in datasets.

This skill enables Claude to conduct end-to-end clustering analysis, from data preprocessing and algorithm selection to execution and visualization. It automates the generation of Python code—utilizing libraries like scikit-learn—to perform tasks such as customer segmentation, anomaly detection, and data structure discovery. Users can simply provide a dataset and a goal, and the skill handles feature scaling, model training, performance metrics like silhouette scores, and visual reporting, making advanced data science workflows accessible through simple natural language commands.

Key Features

01896 GitHub stars

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

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

04Intelligent parameter tuning for optimal cluster identification

05Automated data preprocessing including scaling and normalization

06Visual output generation with scatter plots and cluster labels

Use Cases

01Segmenting customer databases for targeted marketing campaigns

02Discovering inherent structures and groupings in high-dimensional scientific datasets

03Detecting network anomalies or fraudulent transactions using DBSCAN

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills running-clustering-algorithms

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01896 GitHub stars

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

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

04Intelligent parameter tuning for optimal cluster identification

05Automated data preprocessing including scaling and normalization

06Visual output generation with scatter plots and cluster labels

Use Cases

01Segmenting customer databases for targeted marketing campaigns

02Discovering inherent structures and groupings in high-dimensional scientific datasets

03Detecting network anomalies or fraudulent transactions using DBSCAN

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills running-clustering-algorithms

For use in Claude.ai and ChatGPT

Download Skill