>- Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident management.
npx skills add https://github.com/google/skills --skill google-cloud-waf-operational-excellence
The operational excellence pillar in the Google Cloud Well-Architected Framework
provides recommendations to operate workloads efficiently on Google Cloud.
Operational excellence in the cloud involves designing, implementing, and
managing cloud solutions that provide value, performance, security, and
reliability. The recommendations in this pillar help you to continuously improve
and adapt workloads to meet the dynamic and ever-evolving needs in the cloud.
The recommendations in the operational excellence pillar of the Well-Architected
Framework are aligned with the following core principles:
to be considered ready for production, including staffing, processes, and
governance. Grounding document:
https://docs.cloud.google.com/architecture/framework/operational-excellence/operational-readiness-and-performance-using-cloudops.md.txt
incident response, communication, and root cause analysis to minimize impact
and prevent recurrence. Grounding document:
https://docs.cloud.google.com/architecture/framework/operational-excellence/manage-incidents-and-problems.md.txt
right-size environments to maintain performance while ensuring operational
efficiency. Grounding document:
https://docs.cloud.google.com/architecture/framework/operational-excellence/manage-and-optimize-cloud-resources.md.txt
pipelines to ensure consistent, repeatable, and low-risk deployments and
configuration changes. Grounding document:
https://docs.cloud.google.com/architecture/framework/operational-excellence/automate-and-manage-change.md.txt
monitor industry trends, and adapt operations to meet evolving business
needs. Grounding document:
https://docs.cloud.google.com/architecture/framework/operational-excellence/continuously-improve-and-innovate.md.txt
The following are _examples_ of Google Cloud products and features that are
relevant to operational excellence:
hybrid environments.
services.
Objectives (SLOs).
deploying software.
Run, and GCE.
Infrastructure as Code (IaC) automation.
and container images.
and right-sizing opportunities.
organizations, folders, and projects.
for managing operational disruptions.
Ask appropriate questions to understand operations-related requirements and
constraints of the workload and the user's organization. Choose questions from
the following list:
workloads and what specific criteria or metrics do you use?
your critical workloads.
responsibilities, and communication channels.
and implement preventive measures?
workloads, and what tools or techniques do you use?
testing procedures, and deployment strategies.
configuration?
to meet evolving business needs and technological advancements?
Use the following checklist to evaluate the architecture's alignment with
operational excellence recommendations:
readiness before production deployment.
using automated tools.
defined and documented.
major incidents.
Code (IaC) to ensure consistency.
deployment changes.
from Active Assist or performance data.
adapting cloud operations to industry advancements.
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-operational-excellence from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.