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Found 12 Skills
Analyzes Azure costs and provides optimization recommendations including reserved instances, rightsizing, and unused resources. Use when optimizing Azure spending or analyzing Azure costs.
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs and CUDs. Use when optimizing GKE costs, rightsizing GKE workloads, or configuring GKE Spot VMs. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
AWS cost optimization and FinOps workflows. Use for finding unused resources, analyzing Reserved Instance opportunities, detecting cost anomalies, rightsizing instances, evaluating Spot instances, migrating to newer generation instances, implementing FinOps best practices, optimizing storage/network/database costs, and managing cloud financial operations. Includes automated analysis scripts and comprehensive reference documentation.
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
Estimates, analyzes, and optimizes cloud cost across AWS, Google Cloud, Azure, Kubernetes, data platforms, networking, observability, and SaaS. Use for FinOps, cost reviews, unit economics, rightsizing, commitments, budgets, or architecture cost trade-offs.
Optimize cloud infrastructure costs through resource rightsizing, reserved instances, spot instances, and waste reduction strategies.
Analyzes GCP costs and provides optimization recommendations including committed use discounts, rightsizing, and unused resources. Use when optimizing GCP spending or analyzing GCP costs.
Guides FinOps analysis on AWS, GCP, and Azure—cost visibility and allocation, tagging and showback/chargeback models, rightsizing and waste removal, RI/Savings Plan/CUD recommendations, budgets and forecasts, anomaly detection, unit economics (cost per service/customer), and FinOps cadence with engineering accountability. Use when optimizing cloud spend, analyzing CUR/billing exports, building cost dashboards, explaining bill spikes, or improving allocation—not for GL mapping, capex, depreciation, or month-end ledger close (compute-accounting-manager), enterprise EA negotiation (enterprise-cloud-architect), hands-on resource provisioning (cloud-engineer), or hardware supply efficiency (data-center-compute-supply-efficiency).
Reduce AWS spend with rightsizing, autoscaling, commitment planning, and storage lifecycle policies. Use when running FinOps reviews, lowering cloud bills, or improving cost-per-request metrics.
Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, or optimizing individual SQL queries. Do not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).