Cloud Architect

What it does

When your agent uses it

• Designing a multi-region architecture for a critical workload
• Planning a migration into AWS, Azure or GCP in waves
• Cutting cloud spend with right-sizing and reserved capacity
• Defining disaster recovery targets and testing them

Example prompts

• “Design an AWS architecture for our order system with multi-region failover and a cost model.”
• “Plan a lift-and-shift of our data center VMs to Azure using the 6Rs and migration waves.”
• “Review our GCP setup for single points of failure and missing cost allocation tags.”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Discovery — Assess current state, requirements, constraints, compliance needs
2. 2Design — Select services, design topology, plan data architecture
3. 3Security — Implement zero-trust, identity federation, encryption
4. 4Cost Model — Right-size resources, reserved capacity, auto-scaling
5. 5Migration — Apply 6Rs framework, define waves, validate connectivity before cutover
6. 6Operate — Set up monitoring, automation, continuous optimization

What it can do on your machine

Read from SKILL.md and the folder at commit 1be15d8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • aws
  • az

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • synergetic.solutions
  • jeffallan.github.io

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cloud Architect loads about 1.9k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 105 tokens; SKILL.md has 331 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~105

When it runs · the whole SKILL.md, loaded when a task matches

~1.9k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~19k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Jeffallan/claude-skills at commit 1be15d8, republished under its MIT licence (© Jeffallan). 331 words, ~1,882 tokens.

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

metadata.company

https://synergetic.solutions

metadata.version

1.1.0

metadata.domain

infrastructure

metadata.triggers

AWS, Azure, GCP, Google Cloud, cloud migration, cloud architecture, multi-cloud, cloud cost, Well-Architected, landing zone, cloud security, disaster…

metadata.role

architect

metadata.scope

infrastructure

metadata.output-format

architecture

metadata.related-skills

devops-engineer, kubernetes-specialist, terraform-engineer, security-reviewer, microservices-architect, monitoring-expert

# 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}"

# AWS: check target group health in ALB
aws elbv2 describe-target-health \
  --target-group-arn arn:aws:elasticloadbalancing:...

# 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

© Jeffallan, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/cloud-architect of Jeffallan/claude-skills.

• SKILL.md
• references/aws.md
• references/azure.md
• references/cost.md
• references/gcp.md
• references/multi-cloud.md

Open the folder on GitHubat commit 1be15d8