Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.
Works with
Recommends architecture patterns (serverless web, event-driven microservices, three-tier, GraphQL) based on application type, scale, budget, and compliance requirements
Generates production-ready CloudFormation YAML, CDK TypeScript, and Terraform templates with API Gateway, Lambda, DynamoDB, ECS, Aurora, and IAM configurations
Analyzes current AWS costs and identifies optimization op
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaws-solution-architectExecute the skills CLI command in your project's root directory to begin installation:
Fetches aws-solution-architect from alirezarezvani/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate aws-solution-architect. Access via /aws-solution-architect in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.
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)
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:
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.
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 },
});
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:
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
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:
aws cloudformation describe-stack-events \
--stack-name my-app-stack \
--query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'
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:
--capabilities CAPABILITY_IAM and role trust policiesaws cloudformation validate-template --template-body file://template.yaml before deployingGenerates 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
Creates serverless CloudFormation templates.
python scripts/serverless_stack.py --app-name my-app --region us-east-1
Output: Production-ready CloudFormation YAML with:
Analyzes costs and recommends optimizations.
python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000
Output: Recommendations for:
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
alirezarezvani/claude-skills
alirezarezvani/claude-skills
alirezarezvani/claude-skills
alirezarezvani/claude-skills
alirezarezvani/claude-skills
alirezarezvani/claude-skills
We added aws-solution-architect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
aws-solution-architect has been reliable in day-to-day use. Documentation quality is above average for community skills.
aws-solution-architect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: aws-solution-architect is focused, and the summary matches what you get after install.
We added aws-solution-architect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: aws-solution-architect is focused, and the summary matches what you get after install.
Registry listing for aws-solution-architect matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for aws-solution-architect matched our evaluation — installs cleanly and behaves as described in the markdown.
aws-solution-architect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
aws-solution-architect has been reliable in day-to-day use. Documentation quality is above average for community skills.
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