Skill UI

PyMC is a comprehensive Python library for probabilistic programming and Bayesian modeling. It enables users to build, fit, and validate complex statistical models, including hierarchical structures, time series, and regression models. It provides powerful tools for MCMC sampling (NUTS) and variational inference, making it essential for rigorous uncertainty quantification and model comparison (LOO/WAIC).

A risk-paranoid, mandatory review process designed to interrogate any plan that touches sensitive customer data, regulatory compliance, or production access. It forces consideration of critical security pillars including STRIDE threat modeling, blast radius quantification, detection mechanisms (MTTD), incident response runbooks, regulatory notification windows, and third-party vendor security posture. Essential before deploying features handling PII, PHI, or before compliance audits (SOC 2, HIPAA, GDPR).

This skill provides an end-to-end, highly reproducible workflow for bulk RNA-seq differential expression analysis. It orchestrates the entire pipeline, guiding users from raw FASTQ reads through Quality Control (QC), trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and finally executing differential expression testing (pydeseq2) and pathway enrichment analysis. It is designed for robust, statistically sound, and fully documented results.