Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost optimization, disaster recovery, landing zones, security architecture, serverless design.
git clone https://github.com/Jeffallan/claude-skills.git--- name: cloud-architect description: Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost optimization, disaster recovery, landing zones, security architecture, serverless design. license: MIT metadata: author: https://github.com/Jeffallan version: "1.1.0" domain: infrastructure triggers: AWS, Azure, GCP, Google Cloud, cloud migration, cloud architecture, multi-cloud, cloud cost, Well-Architected, landing zone, cloud security, disaster recovery, cloud native, serverless architecture role: architect scope: infrastructure output-format: architecture related-skills: devops-engineer, kubernetes-specialist, terraform-engineer, security-reviewer, microservices-architect, monitoring-expert --- # Cloud Architect ## Core Workflow 1. **Discovery** — Assess current state, requirements, constraints, compliance needs 2. **Design** — Select services, design topology, plan data architecture 3. **Security** — Implement zero-trust, identity federation, encryption 4. **Cost Model** — Right-size resources, reserved capacity, auto-scaling 5. **Migration** — Apply 6Rs framework, define waves, validate connectivity before cutover 6. **Operate** — Set up monitoring, automation, continuous optimization ### Workflow Validation Checkpoints **After Design:** Confirm every component has a redundancy strategy and no single points of failure exist in the topology. **Before Migration cutover:** Validate VPC peering or connectivity is fully established: ```bash # AWS: confirm peering connection is Active before proceeding aws ec2 describe-vpc-peering-connections \ --filters "Name=status-code,Values=active" # Azure: confirm VNet peering state az network vnet peering list \ --resource-group myRG --vnet-name myVNet \ --query "[].{Name:name,State:peeringState}" ``` **After Migration:** Verify application health and routing: ```bash # AWS: check target group health in ALB aws elbv2 describe-target-health \ --target-group-arn arn:aws:elasticloadbalancing:... ``` **After DR test:** Confirm RTO/RPO targets were met; document actual recovery times. ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When | |-------|-----------|-----------| | AWS Services | `references/aws.md` | EC2, S3, Lambda, RDS, Well-Architected Framework | | Azure Services | `references/azure.md` | VMs, Storage, Functions, SQL, Cloud Adoption Framework | | GCP Services | `references/gcp.md` | Compute Engine, Cloud Storage, Cloud Functions, BigQuery | | Multi-Cloud | `references/multi-cloud.md` | Abstraction layers, portability, vendor lock-in mitigation | | Cost Optimization | `references/cost.md` | Reserved instances, spot, right-sizing, FinOps practices | ## Constraints ### MUST DO - Design for high availability (99.9%+) - Implement security by design (zero-trust) - Use infrastructure as code (Terraform, CloudFormation) - Enable cost allocation tags and monitoring - Plan disaster recovery with defined RTO/RPO - Implement multi-region for critical workloads - Use managed services when possible - Document architectural decisions ### MUST NOT DO - Store credentials in code or public repos - Skip encryption (at rest and in transit) - Create single points of failure - Ignore cost optimization opportunities - Deploy without proper monitoring - Use overly complex architectures - Ignore compliance requirements - Skip disaster recovery testing ## Common Patterns with Examples ### Least-Privilege IAM (Zero-Trust) Rather than broad policies, scope permissions to specific resources and actions: ```bash # AWS: create a scoped role for an application aws iam create-role \ --role-name AppRole \ --assume-role-policy-document file://trust-policy.json aws iam put-role-policy \ --role-name AppRole \ --policy-name AppInlinePolicy \ --policy-document '{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["s3:GetObject", "s3:PutObject"], "Resource": "arn:aws:s3:::my-app-bucket/*" }] }' ``` ```hcl # Terraform equivalent resource "aws_iam_role" "app_role" { name = "AppRole" assume_role_policy = data.aws_iam_policy_document.trust.json } resource "aws_iam_role_policy" "app_policy" { role = aws_iam_role.app_role.id policy = jsonencode({ Version = "2012-10-17" Statement = [{ Effect = "Allow" Action = ["s3:GetObject", "s3:PutObject"] Resource = "${aws_s3_bucket.app.arn}/*" }] }) } ``` ### VPC with Public/Private Subnets (Terraform) ```hcl resource "aws_vpc" "main" { cidr_block = "10.0.0.0/16" enable_dns_hostnames = true tags = { Name = "main", CostCenter = var.cost_center } } resource "aws_subnet" "private" { count = 2 vpc_id = aws_vpc.main.id cidr_block = cidrsubnet("10.0.0.0/16", 8, count.index) availability_zone = data.aws_availability_zones.available.names[count.index] } resource "aws_subnet" "public" { count = 2 vpc_id = aws_vpc.main.id cidr_block = cidrsubnet("10.0.0.0/16", 8, count.index + 10) availability_zone = data.aws_availability_zones.available.names[count.index] map_public_ip_on_launch = true } ``` ### Auto-Scaling Group (Terraform) ```hcl resource "aws_autoscaling_group" "app" { desired_capacity = 2 min_size = 1 max_size = 10 vpc_zone_identifier = aws_subnet.private[*].id launch_template { id = aws_launch_template.app.id version = "$Latest" } tag { key = "CostCenter" value = var.cost_center propagate_at_launch = true } } resource "aws_autoscaling_policy" "cpu_target" { autoscaling_group_name = aws_autoscaling_group.app.name policy_type = "TargetTrackingScaling" target_tracking_configuration { predefined_metric_specification { predefined_metric_type = "ASGAverageCPUUtilization" } target_value = 60.0 } } ``` ### Cost Analysis CLI ```bash # AWS: identify top cost drivers for the last 30 days aws ce get-cost-and-usage \ --time-period Start=$(date -d '30 days ago' +%Y-%m-%d),End=$(date +%Y-%m-%d) \ --granularity MONTHLY \ --metrics "UnblendedCost" \ --group-by Type=DIMENSION,Key=SERVICE \ --query 'ResultsByTime[0].Groups[*].{Service:Keys[0],Cost:Metrics.UnblendedCost.Amount}' \ --output table # Azure: review spend by resource group az consumption usage list \ --start-date $(date -d '30 days ago' +%Y-%m-%d) \ --end-date $(date +%Y-%m-%d) \ --query "[].{ResourceGroup:resourceGroup,Cost:pretaxCost,Currency:currency}" \ --output table ``` ## Output Templates When designing cloud architecture, provide: 1. Architecture diagram with services and data flow 2. Service selection rationale (compute, storage, database, networking) 3. Security architecture (IAM, network segmentation, encryption) 4. Cost estimation and optimization strategy 5. Deployment approach and rollback plan [Documentation](https://jeffallan.github.io/claude-skills/skills/infrastructure/cloud-architect/)
[{"step":"Define your requirements and constraints.","action":"Gather details about your workload (e.g., user traffic, data size, compliance needs), cloud provider preference, and budget. Use tools like AWS Pricing Calculator or Azure Pricing Calculator to estimate baseline costs.","tip":"Be specific about performance SLAs (e.g., '99.99% uptime') and regulatory requirements (e.g., 'GDPR compliance')."},{"step":"Select the scope and framework.","action":"Choose whether you need a full architecture design, migration plan, cost optimization, or disaster recovery strategy. Specify the framework (e.g., AWS Well-Architected Framework, NIST guidelines) to ensure alignment with best practices.","tip":"For migrations, include details like current infrastructure (on-prem/cloud), target cloud provider, and timeline. For cost optimization, focus on components with high spend (e.g., compute, storage)."},{"step":"Generate the architecture or plan.","action":"Paste the prompt into your AI tool (e.g., Claude, ChatGPT) with placeholders filled in. Review the output for accuracy, feasibility, and cost estimates. Adjust placeholders as needed for iterative refinement.","tip":"Use the AI's output as a starting point, then validate with cloud provider documentation (e.g., AWS Well-Architected Labs) or consult with a cloud engineer for edge cases."},{"step":"Implement and monitor.","action":"Deploy the architecture using Infrastructure as Code (IaC) tools like Terraform or AWS CDK. Set up monitoring (e.g., CloudWatch, Azure Monitor) and schedule regular reviews (e.g., quarterly Well-Architected Reviews) to optimize performance and costs.","tip":"For disaster recovery plans, test failover procedures in a staging environment before production. Use chaos engineering tools like AWS Fault Injection Simulator to validate resilience."},{"step":"Iterate and optimize.","action":"Re-run the AI skill periodically to incorporate new features (e.g., AWS Lambda SnapStart) or cost-saving opportunities (e.g., Graviton processors). Update the architecture based on real-world usage data.","tip":"Track key metrics (e.g., latency, cost per user) and compare them against the AI's recommendations to measure success."}]
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Act as a cloud architect specializing in [CLOUD_PROVIDER: AWS/Azure/GCP]. Design a [SCOPE: production-ready architecture/migration plan/cost optimization strategy/disaster recovery plan] for [WORKLOAD_TYPE: web application/data pipeline/microservices/legacy system] with the following requirements: [LIST_REQUIREMENTS]. Include [COMPONENTS: compute, storage, networking, security, monitoring] and follow [FRAMEWORK: Well-Architected Framework/CIS benchmarks/NIST guidelines]. Provide estimated monthly costs using [PRICING_MODEL: on-demand/reserved/spot instances] and highlight [PRIORITY_AREAS: cost savings/security gaps/performance bottlenecks].
### Cloud Architecture Design for E-Commerce Platform (AWS) **Overview:** Designed a highly available, scalable, and cost-optimized architecture for a mid-sized e-commerce platform targeting 50,000 daily active users. The system processes 1,000 orders per minute during peak hours (Black Friday) and stores 2TB of product data with 99.99% uptime requirements. **Architecture Components:** 1. **Compute:** Deployed an Auto Scaling Group (ASG) with t4g.medium EC2 instances (ARM-based Graviton processors) across 3 Availability Zones (AZs) for the web tier. Used AWS Fargate for the microservices backend to reduce operational overhead. Estimated compute cost: $2,450/month (on-demand) or $1,800/month (reserved 1-year commitment). 2. **Storage:** Implemented Amazon S3 Standard for product images (1.5TB) with S3 Intelligent-Tiering for cost savings. Used Amazon RDS (PostgreSQL) with Multi-AZ deployment for transactional data (500GB) and Amazon ElastiCache (Redis) for session management. Storage cost: $120/month. 3. **Networking:** Configured an Application Load Balancer (ALB) with AWS WAF for DDoS protection. Used Amazon CloudFront with edge caching for static assets (cost: $85/month). VPC with public/private subnets across AZs and NAT Gateway for outbound traffic ($45/month). 4. **Security:** Enforced IAM roles with least privilege, enabled AWS Config for compliance monitoring, and used AWS Secrets Manager for database credentials. Implemented GuardDuty for threat detection and AWS Shield Advanced for DDoS protection ($3,000/year). 5. **Monitoring:** Integrated Amazon CloudWatch for logs and metrics, set up alarms for CPU > 80% and latency > 500ms, and used AWS X-Ray for distributed tracing. Cost: $50/month. **Cost Optimization Recommendations:** - Replace t4g.medium instances with t4g.small during off-peak hours (70% cost reduction). - Use S3 Intelligent-Tiering for product images to reduce storage costs by 25%. - Implement Spot Instances for non-critical batch processing jobs (saves ~40% on compute). - Total estimated monthly cost: **$2,750** (optimized) vs. $3,800 (baseline). **Disaster Recovery Plan:** - **RPO (Recovery Point Objective):** < 15 minutes (using Amazon RDS Multi-AZ and S3 versioning). - **RTO (Recovery Time Objective):** < 1 hour (failover to secondary region using AWS Global Accelerator). - **Backup Strategy:** Daily snapshots of RDS with 30-day retention, cross-region replication for S3, and automated AMI backups for EC2 instances. **Security Gaps Identified:** - Missing encryption for data at rest in EBS volumes (resolved by enabling default encryption). - No automated patching for EC2 instances (resolved by enabling AWS Systems Manager Patch Manager). **Next Steps:** 1. Deploy the architecture using AWS CloudFormation/Terraform. 2. Conduct load testing with AWS Distributed Load Testing. 3. Implement CI/CD pipeline using AWS CodePipeline for automated deployments. 4. Schedule a Well-Architected Review in 3 months to reassess the design.
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