running-clustering-algorithms

Quick Review

This skill provides a solid foundation for clustering analysis with clear examples and workflow. The description adequately covers when to invoke the skill ('run clustering', 'cluster analysis', 'group data points'), and the SKILL.md explains the process well with concrete examples. Task knowledge is strong - the scripts directory contains dedicated Python files for each algorithm (K-means, DBSCAN, hierarchical), plus supporting modules for data loading, evaluation, and visualization. Structure is reasonable but could be improved: the SKILL.md contains some generic boilerplate sections ('Prerequisites', 'Instructions', 'Output') that add clutter without specific details. The scripts/README.md appears duplicated in the tree. Novelty is moderate - while clustering is a legitimate ML task, it's relatively standard functionality that scikit-learn handles well, and a skilled CLI agent could potentially implement basic clustering with sufficient prompting, though this skill does provide convenience and standardization across multiple algorithms with evaluation metrics.