Skill: alirezarezvani/aws-solution-architect (version 2.1.1)

--- 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: ```bash python scripts/architecture_designer.py --input requirements.json ``` **Example output:** ```json { "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: ```bash # Serverless stack (CloudFormation) python scripts/serverless_stack.py --app-name my-app --region us-east-1 ``` **Example CloudFormation YAML output (core serverless resources):** ```yaml 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):** ```typescript 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: ```bash python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000 ``` **Example output:** ```json { "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: ```bash # 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: ```bash # 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:** 1. Check the failure reason: ```bash aws cloudformation describe-stack-events \ --stack-name my-app-stack \ --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]' ``` 2. Review CloudWatch Logs for Lambda or ECS errors. 3. Fix the template or resource configuration. 4. Delete the failed stack before retrying: ```bash 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. ```bash 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. ```bash 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. ```bash 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:** ```json { "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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