aws-solution-architect
alirezarezvani/claude-skills
Design serverless AWS architectures and generate IaC templates for startups with cost optimization.
What is aws-solution-architect?
AWS Solution Architect helps you design scalable, cost-effective AWS architectures using serverless patterns and infrastructure-as-code. Use it when designing serverless backends, creating CloudFormation/CDK templates, optimizing AWS costs, setting up CI/CD pipelines, or planning migrations to AWS.
- Generate architecture patterns (serverless web, event-driven microservices, three-tier, GraphQL) based on application requirements
- Create production-ready CloudFormation YAML and CDK TypeScript templates with API Gateway, Lambda, DynamoDB, ECS, Aurora, and Cognito
- Estimate monthly AWS costs and identify optimization opportunities (right-sizing, Savings Plans, storage tier transitions)
- Validate deployments and troubleshoot CloudFormation stack failures with detailed error analysis
- Support multi-AZ, compliance-aware designs covering Lambda, API Gateway, DynamoDB, ECS, Aurora, and ElastiCache
How to install aws-solution-architect
npx skills add https://github.com/alirezarezvani/claude-skills --skill aws-solution-architect- AWS account with appropriate IAM permissions (CloudFormation, Lambda, DynamoDB, ECS, RDS, etc.)
- Python 3.8+ for running architecture_designer.py, serverless_stack.py, and cost_optimizer.py scripts
- AWS CLI configured with credentials
- Familiarity with application requirements (user count, requests/sec, budget, compliance needs)
How to use aws-solution-architect
- 1.Gather application requirements: type, expected users, RPS, budget, team AWS experience, compliance needs, and availability SLA
- 2.Run architecture_designer.py with requirements.json to get pattern recommendations (serverless web, microservices, three-tier, GraphQL)
- 3.Review the recommended pattern and validate it matches your team's operational maturity and compliance requirements
- 4.Generate IaC templates using serverless_stack.py (CloudFormation) or CDK snippets for your chosen pattern
- 5.Run cost_optimizer.py on your resource inventory to identify savings opportunities and right-sizing recommendations
- 6.Deploy using CloudFormation, CDK, or Terraform with the generated templates
- 7.Validate deployment status and set up CloudWatch alarms; troubleshoot failures using stack events and logs
Use cases
- Design a serverless MVP backend for a mobile app with minimal ops overhead and <$100/month cost
- Build event-driven microservices using EventBridge, Lambda, SQS, and Step Functions for asynchronous workloads
- Create a three-tier architecture with ECS Fargate, Aurora, and ElastiCache for a SaaS platform scaling to 50k+ users
- Optimize an existing AWS setup to reduce monthly spend by 30% through right-sizing and reserved capacity
- Generate CI/CD pipelines with CodePipeline for automated infrastructure and application deployment
- Startup founders and CTOs designing cloud infrastructure for the first time
- AWS solutions architects and DevOps engineers building IaC templates
- Teams migrating on-premises applications to AWS
- Cost-conscious teams optimizing existing AWS deployments
aws-solution-architect FAQ
Lambda, API Gateway, DynamoDB, ECS Fargate, Aurora (serverless and provisioned), ElastiCache, S3, CloudFront, Cognito, EventBridge, SQS, Step Functions, CloudFormation, and CloudWatch.
The skill generates CloudFormation YAML and CDK TypeScript templates. Terraform support is mentioned but not detailed in the provided documentation.
Estimates are based on service pricing and recommended configurations. The cost_optimizer.py tool provides monthly breakdowns and savings recommendations, but actual costs depend on real usage patterns.
Check stack events for the failure reason, review CloudWatch Logs, validate the template syntax, fix the configuration, delete the failed stack, and redeploy. Common causes include IAM permission errors, resource limits, and invalid template syntax.
The skill supports multi-AZ deployments and compliance-aware designs. Multi-region and advanced DR patterns are not explicitly detailed in the provided documentation.
Full instructions (SKILL.md)
Source of truth, from alirezarezvani/claude-skills.
name: "aws-solution-architect" description: 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.
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.pyand also available inreferences/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_IAMand 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.yamlbefore 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 |
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