task-assignment-problem
Pass
Audited by Gen Agent Trust Hub on Sep 20, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill serves as a reference for mathematical optimization and resource allocation. It provides valid mathematical formulations and Python implementations for standard assignment problems.
- [SAFE]: All identified dependencies (numpy, scipy, pandas, pulp, ortools, sklearn, networkx) are well-known, established libraries in the data science and optimization communities.
- [SAFE]: No evidence of prompt injection, data exfiltration, or persistence mechanisms was found. The skill does not attempt to access sensitive files or perform unauthorized network operations.
- [SAFE]: The code snippets are illustrative and do not include any dynamic code execution patterns (like eval or exec) on untrusted inputs.
- [INDIRECT_PROMPT_INJECTION]: The skill is designed to process user-provided data (cost matrices, task lists).
- Ingestion points: Function parameters in Python snippets (e.g.,
cost_matrix,tasks,workers). - Boundary markers: Not explicitly present in the provided templates.
- Capability inventory: Optimization libraries (SciPy, PuLP) used for mathematical computation; pandas for data manipulation.
- Sanitization: Standard data type handling by libraries like NumPy and pandas, but no specific adversarial sanitization is implemented in the templates.
Audit Metadata