Design AWS architectures for startups using serverless
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.
--- 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: ```
Overview
Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.
Manual install (npx, ZIP)
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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 |
| 1 | |
| 2 | name "aws-solution-architect" |
| 3 | 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. |
| 4 | |
| 5 | |
| 6 | # AWS Solution Architect |
| 7 | |
| 8 | Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates. |
| 9 | |
| 10 | |
| 11 | |
| 12 | ## Workflow |
| 13 | |
| 14 | ### Step 1: Gather Requirements |
| 15 | |
| 16 | Collect application specifications: |
| 17 | |
| 18 | |
| 19 | - Application type (web app, mobile backend, data pipeline, SaaS) |
| 20 | - Expected users and requests per second |
| 21 | - Budget constraints (monthly spend limit) |
| 22 | - Team size and AWS experience level |
| 23 | - Compliance requirements (GDPR, HIPAA, SOC 2) |
| 24 | - Availability requirements (SLA, RPO/RTO) |
| 25 | |
| 26 | |
| 27 | ### Step 2: Design Architecture |
| 28 | |
| 29 | Run the architecture designer to get pattern recommendations: |
| 30 | |
| 31 | |
| 32 | python scripts/architecture_designer.py --input requirements.json |
| 33 | |
| 34 | |
| 35 | **Example output:** |
| 36 | |
| 37 | |
| 38 | { |
| 39 | "recommended_pattern": "serverless_web", |
| 40 | "service_stack": ["S3", "CloudFront", "API Gateway", "Lambda", "DynamoDB", "Cognito"], |
| 41 | "estimated_monthly_cost_usd": 35, |
| 42 | "pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling"], |
| 43 | "cons": ["Cold starts", "15-min Lambda limit", "Eventual consistency"] |
| 44 | } |
| 45 | |
| 46 | |
| 47 | Select from recommended patterns: |
| 48 | **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB |
| 49 | **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions |
| 50 | **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache |
| 51 | **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito |
| 52 | |
| 53 | See `references/architecture_patterns.md` for detailed pattern specifications. |
| 54 | |
| 55 | **Validation checkpoint:** Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3. |
| 56 | |
| 57 | ### Step 3: Generate IaC Templates |
| 58 | |
| 59 | Create infrastructure-as-code for the selected pattern: |
| 60 | |
| 61 | |
| 62 | # Serverless stack (CloudFormation) |
| 63 | python scripts/serverless_stack.py --app-name my-app --region us-east-1 |
| 64 | |
| 65 | |
| 66 | **Example CloudFormation YAML output (core serverless resources):** |
| 67 | |
| 68 | |
| 69 | AWSTemplateFormatVersion: '2010-09-09' |
| 70 | Transform: AWS::Serverless-2016-10-31 |
| 71 | |
| 72 | Parameters: |
| 73 | AppName: |
| 74 | Type: String |
| 75 | Default: my-app |
| 76 | |
| 77 | Resources: |
| 78 | ApiFunction: |
| 79 | Type: AWS::Serverless::Function |
| 80 | Properties: |
| 81 | Handler: index.handler |
| 82 | Runtime: nodejs20.x |
| 83 | MemorySize: 512 |
| 84 | Timeout: 30 |
| 85 | Environment: |
| 86 | Variables: |
| 87 | TABLE_NAME: !Ref DataTable |
| 88 | Policies: |
| 89 | - DynamoDBCrudPolicy: |
| 90 | TableName: !Ref DataTable |
| 91 | Events: |
| 92 | ApiEvent: |
| 93 | Type: Api |
| 94 | Properties: |
| 95 | Path: /{proxy+} |
| 96 | Method: ANY |
| 97 | |
| 98 | DataTable: |
| 99 | Type: AWS::DynamoDB::Table |
| 100 | Properties: |
| 101 | BillingMode: PAY_PER_REQUEST |
| 102 | AttributeDefinitions: |
| 103 | - AttributeName: pk |
| 104 | AttributeType: S |
| 105 | - AttributeName: sk |
| 106 | AttributeType: S |
| 107 | KeySchema: |
| 108 | - AttributeName: pk |
| 109 | KeyType: HASH |
| 110 | - AttributeName: sk |
| 111 | KeyType: RANGE |
| 112 | |
| 113 | |
| 114 | > 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`. |
| 115 | |
| 116 | **Example CDK TypeScript snippet (three-tier pattern):** |
| 117 | |
| 118 | |
| 119 | import * as ecs from 'aws-cdk-lib/aws-ecs'; |
| 120 | import * as ec2 from 'aws-cdk-lib/aws-ec2'; |
| 121 | import * as rds from 'aws-cdk-lib/aws-rds'; |
| 122 | |
| 123 | const vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 }); |
| 124 | |
| 125 | const cluster = new ecs.Cluster(this, 'AppCluster', { vpc }); |
| 126 | |
| 127 | const db = new rds.ServerlessCluster(this, 'AppDb', { |
| 128 | engine: rds.DatabaseClusterEngine.auroraPostgres({ |
| 129 | version: rds.AuroraPostgresEngineVersion.VER_15_2, |
| 130 | }), |
| 131 | vpc, |
| 132 | scaling: { minCapacity: 0.5, maxCapacity: 4 }, |
| 133 | }); |
| 134 | |
| 135 | |
| 136 | ### Step 4: Review Costs |
| 137 | |
| 138 | Analyze estimated costs and optimization opportunities: |
| 139 | |
| 140 | |
| 141 | python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000 |
| 142 | |
| 143 | |
| 144 | **Example output:** |
| 145 | |
| 146 | |
| 147 | { |
| 148 | "current_monthly_usd": 2000, |
| 149 | "recommendations": [ |
| 150 | { "action": "Right-size RDS db.r5.2xlarge → db.r5.large", "savings_usd": 420, "priority": "high" }, |
| 151 | { "action": "Purchase 1-yr Compute Savings Plan at 40% utilization", "savings_usd": 310, "priority": "high" }, |
| 152 | { "action": "Move S3 objects >90 days to Glacier Instant Retrieval", "savings_usd": 85, "priority": "medium" } |
| 153 | ], |
| 154 | "total_potential_savings_usd": 815 |
| 155 | } |
| 156 | |
| 157 | |
| 158 | Output includes: |
| 159 | Monthly cost breakdown by service |
| 160 | Right-sizing recommendations |
| 161 | Savings Plans opportunities |
| 162 | Potential monthly savings |
| 163 | |
| 164 | ### Step 5: Deploy |
| 165 | |
| 166 | Deploy the generated infrastructure: |
| 167 | |
| 168 | |
| 169 | # CloudFormation |
| 170 | aws cloudformation create-stack \ |
| 171 | --stack-name my-app-stack \ |
| 172 | --template-body file://template.yaml \ |
| 173 | --capabilities CAPABILITY_IAM |
| 174 | |
| 175 | # CDK |
| 176 | cdk deploy |
| 177 | |
| 178 | # Terraform |
| 179 | terraform init && terraform apply |
| 180 | |
| 181 | |
| 182 | ### Step 6: Validate and Handle Failures |
| 183 | |
| 184 | Verify deployment and set up monitoring: |
| 185 | |
| 186 | |
| 187 | # Check stack status |
| 188 | aws cloudformation describe-stacks --stack-name my-app-stack |
| 189 | |
| 190 | # Set up CloudWatch alarms |
| 191 | aws cloudwatch put-metric-alarm --alarm-name high-errors ... |
| 192 | |
| 193 | |
| 194 | **If stack creation fails:** |
| 195 | |
| 196 | Check the failure reason: |
| 197 | |
| 198 | aws cloudformation describe-stack-events \ |
| 199 | --stack-name my-app-stack \ |
| 200 | --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]' |
| 201 | |
| 202 | Review CloudWatch Logs for Lambda or ECS errors. |
| 203 | Fix the template or resource configuration. |
| 204 | Delete the failed stack before retrying: |
| 205 | |
| 206 | aws cloudformation delete-stack --stack-name my-app-stack |
| 207 | # Wait for deletion |
| 208 | aws cloudformation wait stack-delete-complete --stack-name my-app-stack |
| 209 | # Redeploy |
| 210 | aws cloudformation create-stack ... |
| 211 | |
| 212 | |
| 213 | **Common failure causes:** |
| 214 | IAM permission errors → verify `--capabilities CAPABILITY_IAM` and role trust policies |
| 215 | Resource limit exceeded → request quota increase via Service Quotas console |
| 216 | Invalid template syntax → run `aws cloudformation validate-template --template-body file://template.yaml` before deploying |
| 217 | |
| 218 | |
| 219 | |
| 220 | ## Tools |
| 221 | |
| 222 | ### architecture_designer.py |
| 223 | |
| 224 | Generates architecture patterns based on requirements. |
| 225 | |
| 226 | |
| 227 | python scripts/architecture_designer.py --input requirements.json --output design.json |
| 228 | |
| 229 | |
| 230 | **Input:** JSON with app type, scale, budget, compliance needs |
| 231 | **Output:** Recommended pattern, service stack, cost estimate, pros/cons |
| 232 | |
| 233 | ### serverless_stack.py |
| 234 | |
| 235 | Creates serverless CloudFormation templates. |
| 236 | |
| 237 | |
| 238 | python scripts/serverless_stack.py --app-name my-app --region us-east-1 |
| 239 | |
| 240 | |
| 241 | **Output:** Production-ready CloudFormation YAML with: |
| 242 | API Gateway + Lambda |
| 243 | DynamoDB table |
| 244 | Cognito user pool |
| 245 | IAM roles with least privilege |
| 246 | CloudWatch logging |
| 247 | |
| 248 | ### cost_optimizer.py |
| 249 | |
| 250 | Analyzes costs and recommends optimizations. |
| 251 | |
| 252 | |
| 253 | python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000 |
| 254 | |
| 255 | |
| 256 | **Output:** Recommendations for: |
| 257 | Idle resource removal |
| 258 | Instance right-sizing |
| 259 | Reserved capacity purchases |
| 260 | Storage tier transitions |
| 261 | NAT Gateway alternatives |
| 262 | |
| 263 | |
| 264 | |
| 265 | ## Quick Start |
| 266 | |
| 267 | ### MVP Architecture (< $100/month) |
| 268 | |
| 269 | |
| 270 | Ask: "Design a serverless MVP backend for a mobile app with 1000 users" |
| 271 | |
| 272 | Result: |
| 273 | - Lambda + API Gateway for API |
| 274 | - DynamoDB pay-per-request for data |
| 275 | - Cognito for authentication |
| 276 | - S3 + CloudFront for static assets |
| 277 | - Estimated: $20-50/month |
| 278 | |
| 279 | |
| 280 | ### Scaling Architecture ($500-2000/month) |
| 281 | |
| 282 | |
| 283 | Ask: "Design a scalable architecture for a SaaS platform with 50k users" |
| 284 | |
| 285 | Result: |
| 286 | - ECS Fargate for containerized API |
| 287 | - Aurora Serverless for relational data |
| 288 | - ElastiCache for session caching |
| 289 | - CloudFront for CDN |
| 290 | - CodePipeline for CI/CD |
| 291 | - Multi-AZ deployment |
| 292 | |
| 293 | |
| 294 | ### Cost Optimization |
| 295 | |
| 296 | |
| 297 | Ask: "Optimize my AWS setup to reduce costs by 30%. Current spend: $3000/month" |
| 298 | |
| 299 | Provide: Current resource inventory (EC2, RDS, S3, etc.) |
| 300 | |
| 301 | Result: |
| 302 | - Idle resource identification |
| 303 | - Right-sizing recommendations |
| 304 | - Savings Plans analysis |
| 305 | - Storage lifecycle policies |
| 306 | - Target savings: $900/month |
| 307 | |
| 308 | |
| 309 | ### IaC Generation |
| 310 | |
| 311 | |
| 312 | Ask: "Generate CloudFormation for a three-tier web app with auto-scaling" |
| 313 | |
| 314 | Result: |
| 315 | - VPC with public/private subnets |
| 316 | - ALB with HTTPS |
| 317 | - ECS Fargate with auto-scaling |
| 318 | - Aurora with read replicas |
| 319 | - Security groups and IAM roles |
| 320 | |
| 321 | |
| 322 | |
| 323 | |
| 324 | ## Input Requirements |
| 325 | |
| 326 | Provide these details for architecture design: |
| 327 | |
| 328 | | Requirement | Description | Example | |
| 329 | |-------------|-------------|---------| |
| 330 | | Application type | What you're building | SaaS platform, mobile backend | |
| 331 | | Expected scale | Users, requests/sec | 10k users, 100 RPS | |
| 332 | | Budget | Monthly AWS limit | $500/month max | |
| 333 | | Team context | Size, AWS experience | 3 devs, intermediate | |
| 334 | | Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 | |
| 335 | | Availability | Uptime requirements | 99.9% SLA, 1hr RPO | |
| 336 | |
| 337 | **JSON Format:** |
| 338 | |
| 339 | |
| 340 | { |
| 341 | "application_type": "saas_platform", |
| 342 | "expected_users": 10000, |
| 343 | "requests_per_second": 100, |
| 344 | "budget_monthly_usd": 500, |
| 345 | "team_size": 3, |
| 346 | "aws_experience": "intermediate", |
| 347 | "compliance": ["SOC2"], |
| 348 | "availability_sla": "99.9%" |
| 349 | } |
| 350 | |
| 351 | |
| 352 | |
| 353 | |
| 354 | ## Output Formats |
| 355 | |
| 356 | ### Architecture Design |
| 357 | |
| 358 | Pattern recommendation with rationale |
| 359 | Service stack diagram (ASCII) |
| 360 | Monthly cost estimate and trade-offs |
| 361 | |
| 362 | ### IaC Templates |
| 363 | |
| 364 | **CloudFormation YAML**: Production-ready SAM/CFN templates |
| 365 | **CDK TypeScript**: Type-safe infrastructure code |
| 366 | **Terraform HCL**: Multi-cloud compatible configs |
| 367 | |
| 368 | ### Cost Analysis |
| 369 | |
| 370 | Current spend breakdown with optimization recommendations |
| 371 | Priority action list (high/medium/low) and implementation checklist |
| 372 | |
| 373 | |
| 374 | |
| 375 | ## Reference Documentation |
| 376 | |
| 377 | | Document | Contents | |
| 378 | |----------|----------| |
| 379 | | `references/architecture_patterns.md` | 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region | |
| 380 | | `references/service_selection.md` | Decision matrices for compute, database, storage, messaging | |
| 381 | | `references/best_practices.md` | Serverless design, cost optimization, security hardening, scalability | |
| 382 |
Details
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- Aug 30, 2026
- License
- MIT
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SkillResume a paused experiment/ar:ar-resume — Resume Experiment
SkillShow experiment dashboard with results, active loops/ar:ar-status — Experiment Dashboard
SkillStart an autonomous experiment loop with user-selected/ar:loop — Autonomous Experiment Loop
SkillRun a single experiment iteration/ar:run — Single Experiment Iteration
SkillSet up a new autoresearch experiment interactively/ar:setup — Create New Experiment
Skill/cs:boardroom <brief/cs:boardroom — Multi-Role Boardroom Deliberation