ml4t-purging-embargo
Pass
Audited by Gen Agent Trust Hub on Sep 13, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill instructions focus on legitimate data science methodologies for time-series validation, specifically providing code to prevent data leakage between training and testing sets.
- [SAFE]: Implementation examples utilize standard, reputable libraries including NumPy and scikit-learn for index manipulation and model evaluation.
- [SAFE]: The reference to the 'ml4t-diagnostic' library and the 'ml4t.diagnostic.splitters' module represents legitimate vendor-owned resources consistent with the author 'ml4t'.
- [SAFE]: No malicious patterns such as network exfiltration, hardcoded credentials, command injection, or obfuscation were identified.
Audit Metadata