Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for build, deploy, and manage the workload cost-efficiently in Google Cloud.
npx skills add https://github.com/google/skills --skill google-cloud-waf-cost-optimization
The Cost Optimization pillar of the Google Cloud Well-Architected Framework
provides a structured approach to optimize the costs of your cloud workloads
while maximizing business value. Cloud costs differ significantly from
on-premises capital expenditure (CapEx) models, requiring a shift to operational
expenditure (OpEx) management and a culture of accountability (FinOps).
The FinOps lifecycle consists of three iterative phases:
reports** for built-in console visibility (filtered by department labels).
Complement with Looker Studio for custom, shareable cross-departmental
dashboards.
The recommendations in the cost optimization pillar of the Well-Architected
Framework are aligned with the following core principles:
resources deliver measurable business value by aligning IT spending with
business objectives. Prioritize investments that directly contribute to
revenue, customer satisfaction, or operational efficiency. Grounding
document:
https://docs.cloud.google.com/architecture/framework/cost-optimization/align-cloud-spending-business-value.md.txt
organization consider the cost impact of their decisions and activities.
Provide teams with the visibility and information they need to make informed,
cost-conscious choices. Grounding document:
https://docs.cloud.google.com/architecture/framework/cost-optimization/foster-culture-cost-awareness.md.txt
pay only for what you consume. Select the most cost-effective resource types,
sizes, and locations that meet your technical and business requirements.
Grounding document:
https://docs.cloud.google.com/architecture/framework/cost-optimization/optimize-resource-usage.md.txt
costs, and proactively make adjustments as needed to optimize your spending.
This iterative approach helps identify and address inefficiencies before they
become significant. Grounding document:
https://docs.cloud.google.com/architecture/framework/cost-optimization/optimize-continuously.md.txt
The following are _examples_ of Google Cloud products and features that are
relevant to cost optimization:
trends. Essential for visibility within the console.
data using SQL and BI tools.
reports. Use alongside Cloud Billing reports for custom visual insights.
reaches predefined thresholds.
cost-saving opportunities.
rightsizing opportunities, and unused commitments.
to help developers and application owners quickly identify their most
expensive, fluctuating, or underutilized cloud resources.
one dashboard.
cost spikes.
Run functions, and GKE Autopilot reduce operational overhead and pay-per-use
scaling.
storage classes (Nearline, Coldline, Archive) based on age or access.
Note: Always recommend Storage Insights first to understand current
access patterns before defining lifecycle rules.
for cost attribution.
environment, team, or application.
regions or machine types) to control costs.
Ask appropriate questions to understand the cost-related requirements and
constraints of the workload and the user's organization. Choose questions from
the following list:
process?
departments?
testing, production)?
ongoing?
Use the following checklist to evaluate the architecture's alignment with
cost-optimization recommendations:
(e.g., env, team, app).
regular cost reviews.
and active alerts.
suggestions provided by Active Assist Recommender.
identified and removed monthly.
unless specific technical constraints exist.
buckets to minimize archival costs. Be aware of retrieval fees
associated with Nearline, Coldline and Archive storage classes.
using Cloud CDN, and leveraging Standard Network Tier where
appropriate. High-volume on-premises traffic uses Direct Peering or
Cloud Interconnect (always verify if hybrid connectivity is involved).
views of spending trends in the console.
customized reporting.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take google/google-cloud-waf-cost-optimization from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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