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.

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Dépôt: jeremylongshore/claude-code-plugins-plus-skills
Dernière activité de la source: 17 juillet 2026 à 22:18
Langue détectée de SKILL.md: anglais
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npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill running-clustering-algorithms

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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

Analyzing the Context: Claude analyzes the user's request to determine the dataset, desired clustering algorithm (if specified), and any specific requirements.
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.
Executing Clustering: The generated code is executed, and the clustering algorithm is applied to the dataset.
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).

When to Use This Skill

This skill activates when you need to:

Identify distinct groups within a dataset.
Perform a cluster analysis to understand data structure.
Run K-means, DBSCAN, or hierarchical clustering on a given dataset.

Examples

Example 1: Customer Segmentation

User request: "Run clustering on this customer data to identify customer segments. The data is in customer_data.csv."

The skill will:

Load the customer_data.csv dataset.
Perform K-means clustering to identify distinct customer segments based on their attributes.
Provide a visualization of the customer segments and their characteristics.

Example 2: Anomaly Detection

User request: "Perform DBSCAN clustering on this network traffic data to identify anomalies. The data is available at network_traffic.txt."

The skill will:

Load the network_traffic.txt dataset.
Perform DBSCAN clustering to identify outliers representing anomalous network traffic.
Report the identified anomalies and their characteristics.

Best Practices

Data Preprocessing: Always preprocess the data (e.g., scaling, normalization) before applying clustering algorithms to improve performance and accuracy.
Algorithm Selection: Choose the appropriate clustering algorithm based on the data characteristics and the desired outcome. K-means is suitable for spherical clusters, while DBSCAN is better for non-spherical clusters and anomaly detection.
Parameter Tuning: Tune the parameters of the clustering algorithm (e.g., number of clusters in K-means, epsilon and min_samples in DBSCAN) to optimize the results.

Integration

This skill can be integrated with data loading skills to retrieve datasets from various sources. It can also be combined with visualization skills to generate insightful visualizations of the clustering results.

Prerequisites

Appropriate file access permissions
Required dependencies installed

Instructions

Invoke this skill when the trigger conditions are met
Provide necessary context and parameters
Review the generated output
Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

Invalid input: Prompts for correction
Missing dependencies: Lists required components
Permission errors: Suggests remediation steps

Resources

Project documentation
Related skills and commands

name	running-clustering-algorithms
description	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.
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version	1.23.0
author	Jeremy Longshore <jeremy@intentsolutions.io>
license	MIT
tags	["ai","clustering-algorithms"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

name	running-clustering-algorithms
description	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.
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version	1.23.0
author	Jeremy Longshore <jeremy@intentsolutions.io>
license	MIT
tags	["ai","clustering-algorithms"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw