AWS Solution Architect

What this skill covers

This page keeps a stable Remote OpenClaw URL for the upstream skillwhile preserving the original source content below. The shell stays consistent, and the body can vary as much as the upstream SKILL.md or README varies.

Source files and registry paths

# 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 |

Related Claude Code skills