Histórico de Auditoria

This is a legitimate ML implementation skill. All 79 static findings are false positives. The scanner misidentified Python code examples (backticks in string literals) as shell commands, and benign ML terminology as cryptographic/network threats. The skill operates in plan mode with user approval for all changes. No malicious patterns, credential theft, or exfiltration behavior detected.

This is a legitimate ML implementation skill. All 79 static findings are false positives. The scanner misidentified Python code examples (backticks in string literals) as shell commands, and benign ML terminology as cryptographic/network threats. The skill operates in plan mode with user approval for all changes. No malicious patterns, credential theft, or exfiltration behavior detected.

This is a legitimate ML implementation skill with standard development capabilities. It operates in plan mode with user approval for all changes. No malicious patterns, credential theft, or exfiltration behavior detected. Risk comes only from standard development tool capabilities.

This is a legitimate ML implementation skill with standard development capabilities. It operates in plan mode with user approval for all changes. No malicious patterns, credential theft, or exfiltration behavior detected. Risk comes only from standard development tool capabilities.

This is a legitimate ML implementation skill with standard development capabilities. It operates in plan mode with user approval for all changes. No malicious patterns, credential theft, or exfiltration behavior detected. Risk comes only from standard development tool capabilities.