The Agent Skills Directory

[SAFE]: The skill acts as an educational and technical reference for the scikit-learn library, containing standard import patterns, best practice guides, and extensive documentation on ML algorithms.
[EXTERNAL_DOWNLOADS]: The skill includes instructions to install industry-standard Python libraries from official registries. All suggested packages are well-known and essential for the described tasks.
Evidence: scikit-learn, pandas, numpy, matplotlib, and seaborn in SKILL.md.
Evidence: imbalanced-learn in references/model_evaluation.md.
Evidence: category-encoders in references/preprocessing.md.
Evidence: umap-learn in references/unsupervised_learning.md.
[COMMAND_EXECUTION]: The provided scripts (clustering_analysis.py) execute local shell commands to save visualization plots to the file system. These operations are transparent, follow standard naming conventions (e.g., clustering_results.png), and are consistent with the skill's purpose.
[DATA_EXPOSURE]: Documentation describes standard model serialization procedures using joblib and pickle. No evidence of unauthorized file access, credential harvesting, or exfiltration of sensitive data was found.

scikit-learn