Automate Cloud and DevOps Workflows

This workflow orchestrates a comprehensive cloud and DevOps process across seven phases, each invoking specific specialist skills via copy-paste prompts. Phase 1 (Cloud Infrastructure Setup) uses cloud-architect, aws-skills, azure-functions, gcp-cloud-run, and terraform-skill/terraform-specialist to design architecture, set up accounts/billing, configure networking, provision resources, and set up IAM. Phase 2 (Container Orchestration) uses kubernetes-architect, docker-expert, helm-chart-scaffolding, k8s-manifest-generator, and k8s-security-policies to design container architecture, build images, write K8s manifests, and deploy with networking. Phase 3 (CI/CD Implementation) uses deployment-engineer, cicd-automation-workflow-automate, github-actions-templates, gitlab-ci-patterns, and deployment-pipeline-design to build pipelines with test automation, deployment stages, rollback strategies, and notifications.

Phase 4 (Monitoring and Observability) uses observability-engineer, grafana-dashboards, prometheus-configuration, datadog-automation, and sentry-automation to set up metrics, log aggregation, distributed tracing, dashboards, and alerts. Phase 5 (Cloud Security) uses cloud-penetration-testing, aws-penetration-testing, k8s-security-policies, secrets-management, and mtls-configuration to assess security, configure security groups, secrets, network policies, encryption, and audit logging. Phase 6 (Cost Optimization) uses cost-optimization and database-cloud-optimization-cost-optimize to analyze spending, right-size resources, apply auto-scaling and reserved instances, and set cost alerts. Phase 7 (Disaster Recovery) uses incident-responder, incident-runbook-templates, and postmortem-writing to design DR strategy, set up backups, create runbooks, test failover, and document procedures.

It also documents provider-specific skill/service groupings: AWS (aws-skills, aws-serverless, aws-penetration-testing; EC2/Lambda/S3/RDS/ECS/EKS), Azure (azure-functions, azure-ai-projects-py, azure-monitor-opentelemetry-py; Functions/App Service/AKS/Cosmos DB), and GCP (gcp-cloud-run; Cloud Run/GKE/Cloud Functions/BigQuery).

Use @cloud-architect to design multi-cloud architecture
Use @terraform-skill to provision AWS infrastructure
Use @kubernetes-architect to design K8s architecture
Use @docker-expert to containerize application

Use this workflow when setting up cloud infrastructure, implementing CI/CD pipelines, deploying Kubernetes applications, configuring monitoring and observability, managing cloud costs, or implementing broader DevOps practices end to end.

Only use it when the task clearly matches this scope; it is not a substitute for environment-specific validation, testing, or expert review. Related bundles cover adjacent scope: development (application development), security-audit (security testing), database (database operations), and testing-qa (testing workflows).

Inputs: a cloud/DevOps initiative spanning infrastructure, containers, CI/CD, monitoring, security, cost, and/or disaster recovery.

Outputs: provisioned infrastructure, a working CI/CD pipeline, configured monitoring, security measures in place, applied cost optimization, and documented DR procedures - tracked against explicit quality gates for each.

cloud-architect, aws-skills, azure-functions, gcp-cloud-run, terraform-skill/terraform-specialist, kubernetes-architect, docker-expert, helm-chart-scaffolding, github-actions-templates, gitlab-ci-patterns, observability-engineer, grafana-dashboards, prometheus-configuration, datadog-automation, sentry-automation, cloud-penetration-testing, secrets-management, cost-optimization, incident-responder, incident-runbook-templates.

Teams running a full cloud/DevOps lifecycle - from infrastructure provisioning through container orchestration, CI/CD, monitoring, security, cost optimization, and disaster recovery - who want a phased workflow chaining the relevant specialist skills.