The Agent Skills Directory

[DYNAMIC_EXECUTION] (MEDIUM): The file scripts/classification_pipeline.py utilizes joblib.dump for model persistence. While saving a model is generally safe, the joblib library (similar to pickle) is inherently insecure when loading files from untrusted sources, as it can be leveraged to execute arbitrary code during deserialization.
[INDIRECT_PROMPT_INJECTION] (LOW): The skill is designed to process external datasets for training and analysis (e.g., in classification_pipeline.py and clustering_analysis.py).
Ingestion points: Dataframes and feature matrices passed to the training and clustering functions.
Boundary markers: None identified; the scripts assume well-formatted data for scikit-learn.
Capability inventory: Local file writes for plots (.png) and models (.pkl). No network or system command capabilities.
Sanitization: Standard preprocessing (scaling, imprinting) is present, but no security-focused validation of data content is performed.
Assessment: The vulnerability surface is limited because the skill lacks high-privilege capabilities like network access or arbitrary command execution, but malicious data could still influence downstream agent reasoning.

scikit-learn