running-clustering-algorithms

name	running-clustering-algorithms
description	Segment data with clustering algorithms such as K-means, DBSCAN, or hierarchical clustering. Use for unsupervised grouping and cluster diagnostics, not supervised classification or publication-figure ownership.
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version	1.0.0
author	Jeremy Longshore <[e-mail]>
license	MIT

Clustering Algorithm Runner

Use this skill when the main question is how to group unlabeled data points.

Overview

This skill covers algorithm choice, preprocessing implications, cluster validation, and interpretation for unsupervised segmentation problems.

When to Use This Skill

• Customer segmentation, cohort discovery, or grouping unlabeled records
• Choosing between centroid, density, or hierarchical clustering
• Reviewing silhouette score, Davies-Bouldin, or cluster stability

Not For / Boundaries

• Supervised prediction with labels: use training-machine-learning-models
• Pure anomaly review without clustering as the central method: use anomaly-detector
• Final narrative report packaging: use scientific-reporting

Typical Outputs

• Algorithm recommendation with parameter guidance
• Cluster-assignment workflow
• Validation and interpretation notes for cluster quality

Related Skills

• creating-data-visualizations for exploratory plots of cluster structure
• anomaly-detector when outliers become the next question