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.
npx skills add https://github.com/Jeffallan/claude-skills --skill cloud-architect
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:
# 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:
# 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.
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 |
Rather than broad policies, scope permissions to specific resources and actions:
# 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/*"
}]
}'
# 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 = "${aws_s3_bucket.app.arn}/*"
}]
})
}
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
enable_dns_hostnames = true
tags = { Name = "main", CostCenter = var.cost_center }
}
resource "aws_subnet" "private" {
count = 2
vpc_id = aws_vpc.main.id
cidr_block = cidrsubnet("10.0.0.0/16", 8, count.index)
availability_zone = data.aws_availability_zones.available.names[count.index]
}
resource "aws_subnet" "public" {
count = 2
vpc_id = aws_vpc.main.id
cidr_block = cidrsubnet("10.0.0.0/16", 8, count.index + 10)
availability_zone = data.aws_availability_zones.available.names[count.index]
map_public_ip_on_launch = true
}
resource "aws_autoscaling_group" "app" {
desired_capacity = 2
min_size = 1
max_size = 10
vpc_zone_identifier = aws_subnet.private[*].id
launch_template {
id = aws_launch_template.app.id
version = "$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
}
}
# 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
When designing cloud architecture, provide:
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.
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