Use when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or…
DevOps for CI/CD, containers, Kubernetes, and Terraform. Use when building pipelines, containerizing apps, managing clusters, provisioning cloud infra, deploying with blue- green/canary, or handling infrastructure incidents.
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.
Map, measure and improve business processes — SIPOC and swimlane capture, cycle-time and bottleneck analysis, handoff diagnosis, and a payback-ranked improvement backlog. Use when a process is slow, error-prone, or crosses too many teams.
Cut software and services spend through seat-utilisation analysis, redundant-tool detection, and renewal-timing leverage. Use when auditing SaaS spend, preparing a renewal negotiation, or hunting a budget-reduction target.
Vendor lifecycle — weighted selection scorecards, risk tiering, renewal and notice-deadline tracking, spend concentration, and SLA credits. Use when selecting a vendor, preparing a renewal, or reviewing a vendor portfolio.
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
Test and evaluation harness for AI agents — scenario suites, deterministic replay, regression diffing, cost and latency budgets. Use when agent quality is vibe-checked, before shipping a prompt or model change, or when evals drift.