aws-solution-architect

Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.

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SKILL.md

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Evals

AWS Solution Architect

Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.

Workflow

Step 1: Gather Requirements

Collect application specifications:

- Application type (web app, mobile backend, data pipeline, SaaS)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and AWS experience level
- Compliance requirements (GDPR, HIPAA, SOC 2)
- Availability requirements (SLA, RPO/RTO)

Step 2: Design Architecture

Run the architecture designer to get pattern recommendations:

python scripts/architecture_designer.py --input requirements.json

Example output:

{
  "recommended_pattern": "serverless_web",
  "service_stack": ["S3", "CloudFront", "API Gateway", "Lambda", "DynamoDB", "Cognito"],
  "estimated_monthly_cost_usd": 35,
  "pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling"],
  "cons": ["Cold starts", "15-min Lambda limit", "Eventual consistency"]
}

Select from recommended patterns:

Serverless Web: S3 + CloudFront + API Gateway + Lambda + DynamoDB
Event-Driven Microservices: EventBridge + Lambda + SQS + Step Functions
Three-Tier: ALB + ECS Fargate + Aurora + ElastiCache
GraphQL Backend: AppSync + Lambda + DynamoDB + Cognito

See references/architecture_patterns.md for detailed pattern specifications.

Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

Step 3: Generate IaC Templates

Create infrastructure-as-code for the selected pattern:

# Serverless stack (CloudFormation)
python scripts/serverless_stack.py --app-name my-app --region us-east-1

Example CloudFormation YAML output (core serverless resources):

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Parameters:
  AppName:
    Type: String
    Default: my-app

Resources:
  ApiFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: nodejs20.x
      MemorySize: 512
      Timeout: 30
      Environment:
        Variables:
          TABLE_NAME: !Ref DataTable
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref DataTable
      Events:
        ApiEvent:
          Type: Api
          Properties:
            Path: /{proxy+}
            Method: ANY

  DataTable:
    Type: AWS::DynamoDB::Table
    Properties:
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: pk
          AttributeType: S
        - AttributeName: sk
          AttributeType: S
      KeySchema:
        - AttributeName: pk
          KeyType: HASH
        - AttributeName: sk
          KeyType: RANGE

Full templates including API Gateway, Cognito, IAM roles, and CloudWatch logging are generated by serverless_stack.py and also available in references/architecture_patterns.md.

Example CDK TypeScript snippet (three-tier pattern):

import * as ecs from 'aws-cdk-lib/aws-ecs';
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import * as rds from 'aws-cdk-lib/aws-rds';

const vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 });

const cluster = new ecs.Cluster(this, 'AppCluster', { vpc });

const db = new rds.ServerlessCluster(this, 'AppDb', {
  engine: rds.DatabaseClusterEngine.auroraPostgres({
    version: rds.AuroraPostgresEngineVersion.VER_15_2,
  }),
  vpc,
  scaling: { minCapacity: 0.5, maxCapacity: 4 },
});

Step 4: Review Costs

Analyze estimated costs and optimization opportunities:

python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

Example output:

{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size RDS db.r5.2xlarge → db.r5.large", "savings_usd": 420, "priority": "high" },
    { "action": "Purchase 1-yr Compute Savings Plan at 40% utilization", "savings_usd": 310, "priority": "high" },
    { "action": "Move S3 objects >90 days to Glacier Instant Retrieval", "savings_usd": 85, "priority": "medium" }
  ],
  "total_potential_savings_usd": 815
}

Output includes:

Monthly cost breakdown by service
Right-sizing recommendations
Savings Plans opportunities
Potential monthly savings

Step 5: Deploy

Deploy the generated infrastructure:

# CloudFormation
aws cloudformation create-stack \
  --stack-name my-app-stack \
  --template-body file://template.yaml \
  --capabilities CAPABILITY_IAM

# CDK
cdk deploy

# Terraform
terraform init && terraform apply

Step 6: Validate and Handle Failures

Verify deployment and set up monitoring:

# Check stack status
aws cloudformation describe-stacks --stack-name my-app-stack

# Set up CloudWatch alarms
aws cloudwatch put-metric-alarm --alarm-name high-errors ...

If stack creation fails:

Check the failure reason:

aws cloudformation describe-stack-events \
  --stack-name my-app-stack \
  --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'

Review CloudWatch Logs for Lambda or ECS errors.
Fix the template or resource configuration.

Delete the failed stack before retrying:

aws cloudformation delete-stack --stack-name my-app-stack
# Wait for deletion
aws cloudformation wait stack-delete-complete --stack-name my-app-stack
# Redeploy
aws cloudformation create-stack ...

Common failure causes:

IAM permission errors → verify --capabilities CAPABILITY_IAM and role trust policies
Resource limit exceeded → request quota increase via Service Quotas console
Invalid template syntax → run aws cloudformation validate-template --template-body file://template.yaml before deploying

Tools

architecture_designer.py

Generates architecture patterns based on requirements.

python scripts/architecture_designer.py --input requirements.json --output design.json

Input: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons

serverless_stack.py

Creates serverless CloudFormation templates.

python scripts/serverless_stack.py --app-name my-app --region us-east-1

Output: Production-ready CloudFormation YAML with:

API Gateway + Lambda
DynamoDB table
Cognito user pool
IAM roles with least privilege
CloudWatch logging

cost_optimizer.py

Analyzes costs and recommends optimizations.

python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000

Output: Recommendations for:

Idle resource removal
Instance right-sizing
Reserved capacity purchases
Storage tier transitions
NAT Gateway alternatives

Quick Start

MVP Architecture (< $100/month)

Ask: "Design a serverless MVP backend for a mobile app with 1000 users"

Result:
- Lambda + API Gateway for API
- DynamoDB pay-per-request for data
- Cognito for authentication
- S3 + CloudFront for static assets
- Estimated: $20-50/month

Scaling Architecture ($500-2000/month)

Ask: "Design a scalable architecture for a SaaS platform with 50k users"

Result:
- ECS Fargate for containerized API
- Aurora Serverless for relational data
- ElastiCache for session caching
- CloudFront for CDN
- CodePipeline for CI/CD
- Multi-AZ deployment

Cost Optimization

Ask: "Optimize my AWS setup to reduce costs by 30%. Current spend: $3000/month"

Provide: Current resource inventory (EC2, RDS, S3, etc.)

Result:
- Idle resource identification
- Right-sizing recommendations
- Savings Plans analysis
- Storage lifecycle policies
- Target savings: $900/month

IaC Generation

Ask: "Generate CloudFormation for a three-tier web app with auto-scaling"

Result:
- VPC with public/private subnets
- ALB with HTTPS
- ECS Fargate with auto-scaling
- Aurora with read replicas
- Security groups and IAM roles

Input Requirements

Provide these details for architecture design:

Requirement	Description	Example
Application type	What you're building	SaaS platform, mobile backend
Expected scale	Users, requests/sec	10k users, 100 RPS
Budget	Monthly AWS limit	$500/month max
Team context	Size, AWS experience	3 devs, intermediate
Compliance	Regulatory needs	HIPAA, GDPR, SOC 2
Availability	Uptime requirements	99.9% SLA, 1hr RPO

JSON Format:

{
  "application_type": "saas_platform",
  "expected_users": 10000,
  "requests_per_second": 100,
  "budget_monthly_usd": 500,
  "team_size": 3,
  "aws_experience": "intermediate",
  "compliance": ["SOC2"],
  "availability_sla": "99.9%"
}

Output Formats

Architecture Design

Pattern recommendation with rationale
Service stack diagram (ASCII)
Monthly cost estimate and trade-offs

IaC Templates

CloudFormation YAML: Production-ready SAM/CFN templates
CDK TypeScript: Type-safe infrastructure code
Terraform HCL: Multi-cloud compatible configs

Cost Analysis

Current spend breakdown with optimization recommendations
Priority action list (high/medium/low) and implementation checklist

Reference Documentation

Document	Contents
`references/architecture_patterns.md`	6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region
`references/service_selection.md`	Decision matrices for compute, database, storage, messaging
`references/best_practices.md`	Serverless design, cost optimization, security hardening, scalability

Repository: alirezarezvani/claude-skills
Commit: a028cc7
Last updated: 1 day ago
Created: 1 day ago

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