skills/jeremylongshore/claude-code-plugins/engineering-features-for-machine-learning/Gen Agent Trust Hub
engineering-features-for-machine-learning
Warn
Audited by Gen Agent Trust Hub on Jul 21, 2026
Risk Level: MEDIUMPROMPT_INJECTIONCOMMAND_EXECUTION
Full Analysis
- [PROMPT_INJECTION]: The script
scripts/feature_importance_analyzer.pycontains deceptive metadata. While its internal docstring, header, and help description claim it performs feature importance analysis (SHAP and permutation importance), the actual code is a generic directory scanner that recursively traverses the filesystem. This discrepancy is a form of metadata poisoning that could mislead the AI agent into executing filesystem operations under false pretenses. - [COMMAND_EXECUTION]: The skill documentation in
scripts/README.mdlists multiple core scripts (e.g.,feature_engineering_pipeline.py,data_visualizer.py) as completed and existing, but they are not present in the provided file set. This lack of package integrity, combined with the deceptive script, presents a risk of unintended behavior when the agent attempts to invoke missing or mislabeled tools. - [PROMPT_INJECTION]: The skill processes external data (e.g.,
assets/example_dataset.csv), creating an indirect prompt injection surface. - Ingestion points: Data is loaded via
pd.read_csv()inassets/feature_engineering_template.py. - Boundary markers: Absent; there are no instructions to prevent the agent from interpreting content within the data as directives.
- Capability inventory: The skill possesses extensive execution and filesystem traversal capabilities through the
Bash(cmd:*)tool and thefeature_importance_analyzer.pyscript. - Sanitization: No validation or sanitization of input data content is implemented in the provided scripts.
Audit Metadata