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All 383 static findings are false positives. The 'weak cryptographic algorithm' detections flag legitimate PyMC probability distributions. The 'external_commands' findings flag markdown backtick syntax. This is a legitimate scientific computing skill for Bayesian statistical modeling.

All 383 static findings are false positives. The 'weak cryptographic algorithm' detections flag legitimate PyMC probability distributions. The 'external_commands' findings flag markdown backtick syntax. This is a legitimate scientific computing skill for Bayesian statistical modeling.

The static analysis findings are false positives. The 'weak cryptographic algorithm' detections are actually legitimate PyMC probability distributions (Normal, HalfNormal, etc.) being documented, not cryptographic code. The 'external_commands' findings are documentation examples showing shell commands to users, not actual code execution. This is a legitimate scientific computing skill for Bayesian modeling.

Legitimate scientific computing skill with Python scripts for model comparison and diagnostics. All code uses standard libraries (PyMC, ArviZ, NumPy, Matplotlib) appropriate for Bayesian analysis. No network access, credential harvesting, or suspicious capabilities detected.