Individual skill
running-clustering-algorithms
Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.
plugins/ai-ml/clustering-algorithm-runner/skills/running-clustering-algorithms/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- 2,916
- Repository last updated
- Nov 12, 2026
- License
- MIT
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
- clustering-algorithms Declared skill tag
- engineering Derived category
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SKILL.mdcore · 4,066 bytesassets/README.mddoc · 377 bytesassets/clustering_visualization.pyruns · 3,582 bytesassets/config_template.jsonasset · 2,755 bytesassets/example_data.csvasset · 865 bytesreferences/README.mddoc · 70 bytesscripts/README.mddoc · 626 bytes
Read excerpt
# Clustering Algorithm Runner
Run clustering algorithms (K-means, DBSCAN, hierarchical) on datasets to discover natural groupings and structure in data.
## Overview
This skill empowers Claude to perform clustering analysis on provided datasets. It allows for automated execution of various clustering algorithms, providing insights into data groupings and structures.
## How It Works
1. **Analyzing the Context**: Claude analyzes the user's request to determine the dataset, desired clustering algorithm (if specified), and any specific requirements.
2. **Generating Code**: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn) to perform the clustering task, including data loading, preprocessing, algorithm execution, and result visualization.
3. **Executing Clustering**: The generated code is executed, and the clustering algorithm is applied to the dataset.
4. **Providing Results**: Claude presents the results, including cluster assignments, performance metrics (e.g., silhouette score, Davies-Bouldin index), and visualizations (e.g., scatter plots with cluster labels).
