running-clustering-algorithms

Quick Review

This clustering skill provides a solid foundation with clear descriptions of K-means, DBSCAN, and hierarchical clustering capabilities. The SKILL.md effectively describes when and how to use the skill with concrete examples. Task knowledge is strong, with referenced Python scripts for each algorithm type, a data loader, evaluator, and configuration template. Structure is reasonable but could be improved—the SKILL.md contains some generic boilerplate sections (Prerequisites, Instructions, Output) that lack specificity. The scripts/ directory appears well-organized with dedicated modules. Novelty is moderate: while clustering requires ML expertise and proper parameter tuning, the fundamental task of running standard clustering algorithms is relatively straightforward for modern CLI agents with access to scikit-learn documentation. The skill adds value through pre-built scripts and evaluation metrics, but doesn't represent highly complex or token-intensive functionality that would be difficult for an agent to reproduce.