DuqueOM

Recurring 23-domain enterprise/ISO repository audit — governance, traceability, security, supply chain, reproducibility, observability, architecture, technical debt, DX — produces a findings report with file:line evidence and risk ratings, and preserves the audit bar via anti-patterns Q-01…Q-08

Recurring 23-domain enterprise/ISO repository audit — governance, traceability, security, supply chain, reproducibility, observability, architecture, technical debt, DX — produces a findings report with file:line evidence and risk ratings, and preserves the audit bar via anti-patterns Q-01…Q-08 (Mode: AUTO — AGENTS.md Agent Behavior Protocol applies.)

Review cloud costs against budget and identify optimization opportunities

Debug ML inference issues — latency spikes, wrong predictions, event loop blocking

Scan a service for edge-protection coverage (WAF, DDoS mitigation, rate limiting) against ADR-042/D-38 — produces a PASS/FAIL report with file:line evidence

Scan a service for edge-protection coverage (WAF, DDoS mitigation, rate limiting) against ADR-042/D-38 — produces a PASS/FAIL report with file:line evidence (Mode: AUTO — AGENTS.md Agent Behavior Protocol applies.)

Systematic root-cause diagnosis for any non-ML-serving bug (CI, infra, agentic tooling, drift scripts) — reproduce, minimize, hypothesize, instrument, fix, regression-test

Structured, blameless post-incident review after /incident, /rollback, or /secret-breach closes — timeline reconstruction from primary sources, 5-whys root cause, action items with owner and date, written to docs/incidents/

Scaffold and run batch scoring jobs (CronJob + Parquet output) that reuse the service's model + feature-engineering code without opening the live API

Root-cause a performance alert using sliced metrics + ground-truth

Review cloud costs against budget and identify optimization opportunities

Debug ML inference issues — latency spikes, wrong predictions, event loop blocking

Deploy ML service to EKS with Kustomize overlays and IRSA

Deploy ML service to GKE with Kustomize overlays and Workload Identity

Run and interpret DATA drift (PSI) AND CONCEPT drift (sliced performance) for an ML service

Run 6-phase exploratory data analysis on a new dataset — ingest, profile, univariate, correlations, leakage gate, feature proposals