Complete restructure based on AI Agent Skills Benchmark feedback (original score: 66/100):
## Directory Reorganization
- Moved Python scripts to scripts/ directory
- Moved sample files to assets/ directory
- Created references/ directory with extracted content
- Removed HOW_TO_USE.md (integrated into SKILL.md)
- Removed __pycache__
## New Reference Files (3 files)
- architecture_patterns.md: 6 AWS patterns (serverless, microservices, three-tier,
data processing, GraphQL, multi-region) with diagrams, cost breakdowns, pros/cons
- service_selection.md: Decision matrices for compute, database, storage, messaging,
networking, security services with code examples
- best_practices.md: Serverless design, cost optimization, security hardening,
scalability patterns, common pitfalls
## SKILL.md Rewrite
- Reduced from 345 lines to 307 lines (moved patterns to references/)
- Added trigger phrases to description ("design serverless architecture",
"create CloudFormation templates", "optimize AWS costs")
- Structured around 6-step workflow instead of encyclopedia format
- Added Quick Start examples (MVP, Scaling, Cost Optimization, IaC)
- Removed marketing language ("Expert", "comprehensive")
- Consistent imperative voice throughout
## Structure Changes
- scripts/: architecture_designer.py, cost_optimizer.py, serverless_stack.py
- references/: architecture_patterns.md, service_selection.md, best_practices.md
- assets/: sample_input.json, expected_output.json
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
307 lines
7.8 KiB
Markdown
307 lines
7.8 KiB
Markdown
---
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name: aws-solution-architect
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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.
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---
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# AWS Solution Architect
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Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.
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---
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## Table of Contents
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- [Trigger Terms](#trigger-terms)
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- [Workflow](#workflow)
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- [Tools](#tools)
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- [Quick Start](#quick-start)
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- [Input Requirements](#input-requirements)
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- [Output Formats](#output-formats)
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---
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## Trigger Terms
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Use this skill when you encounter:
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| Category | Terms |
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|----------|-------|
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| **Architecture Design** | serverless architecture, AWS architecture, cloud design, microservices, three-tier |
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| **IaC Generation** | CloudFormation, CDK, Terraform, infrastructure as code, deploy template |
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| **Serverless** | Lambda, API Gateway, DynamoDB, Step Functions, EventBridge, AppSync |
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| **Containers** | ECS, Fargate, EKS, container orchestration, Docker on AWS |
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| **Cost Optimization** | reduce AWS costs, optimize spending, right-sizing, Savings Plans |
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| **Database** | Aurora, RDS, DynamoDB design, database migration, data modeling |
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| **Security** | IAM policies, VPC design, encryption, Cognito, WAF |
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| **CI/CD** | CodePipeline, CodeBuild, CodeDeploy, GitHub Actions AWS |
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| **Monitoring** | CloudWatch, X-Ray, observability, alarms, dashboards |
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| **Migration** | migrate to AWS, lift and shift, replatform, DMS |
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---
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## Workflow
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### Step 1: Gather Requirements
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Collect application specifications:
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```
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- Application type (web app, mobile backend, data pipeline, SaaS)
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- Expected users and requests per second
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- Budget constraints (monthly spend limit)
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- Team size and AWS experience level
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- Compliance requirements (GDPR, HIPAA, SOC 2)
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- Availability requirements (SLA, RPO/RTO)
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```
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### Step 2: Design Architecture
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Run the architecture designer to get pattern recommendations:
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```bash
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python scripts/architecture_designer.py --input requirements.json
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```
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Select from recommended patterns:
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- **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB
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- **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions
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- **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache
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- **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito
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See `references/architecture_patterns.md` for detailed pattern specifications.
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### Step 3: Generate IaC Templates
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Create infrastructure-as-code for the selected pattern:
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```bash
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# Serverless stack (CloudFormation)
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python scripts/serverless_stack.py --app-name my-app --region us-east-1
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# Output: CloudFormation YAML template ready to deploy
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```
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### Step 4: Review Costs
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Analyze estimated costs and optimization opportunities:
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```bash
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python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000
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```
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Output includes:
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- Monthly cost breakdown by service
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- Right-sizing recommendations
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- Savings Plans opportunities
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- Potential monthly savings
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### Step 5: Deploy
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Deploy the generated infrastructure:
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```bash
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# CloudFormation
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aws cloudformation create-stack \
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--stack-name my-app-stack \
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--template-body file://template.yaml \
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--capabilities CAPABILITY_IAM
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# CDK
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cdk deploy
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# Terraform
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terraform init && terraform apply
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```
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### Step 6: Validate
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Verify deployment and set up monitoring:
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```bash
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# Check stack status
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aws cloudformation describe-stacks --stack-name my-app-stack
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# Set up CloudWatch alarms
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aws cloudwatch put-metric-alarm --alarm-name high-errors ...
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```
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---
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## Tools
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### architecture_designer.py
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Generates architecture patterns based on requirements.
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```bash
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python scripts/architecture_designer.py --input requirements.json --output design.json
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```
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**Input:** JSON with app type, scale, budget, compliance needs
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**Output:** Recommended pattern, service stack, cost estimate, pros/cons
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### serverless_stack.py
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Creates serverless CloudFormation templates.
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```bash
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python scripts/serverless_stack.py --app-name my-app --region us-east-1
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```
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**Output:** Production-ready CloudFormation YAML with:
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- API Gateway + Lambda
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- DynamoDB table
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- Cognito user pool
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- IAM roles with least privilege
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- CloudWatch logging
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### cost_optimizer.py
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Analyzes costs and recommends optimizations.
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```bash
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python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000
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```
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**Output:** Recommendations for:
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- Idle resource removal
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- Instance right-sizing
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- Reserved capacity purchases
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- Storage tier transitions
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- NAT Gateway alternatives
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---
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## Quick Start
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### MVP Architecture (< $100/month)
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```
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Ask: "Design a serverless MVP backend for a mobile app with 1000 users"
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Result:
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- Lambda + API Gateway for API
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- DynamoDB pay-per-request for data
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- Cognito for authentication
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- S3 + CloudFront for static assets
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- Estimated: $20-50/month
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```
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### Scaling Architecture ($500-2000/month)
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```
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Ask: "Design a scalable architecture for a SaaS platform with 50k users"
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Result:
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- ECS Fargate for containerized API
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- Aurora Serverless for relational data
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- ElastiCache for session caching
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- CloudFront for CDN
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- CodePipeline for CI/CD
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- Multi-AZ deployment
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```
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### Cost Optimization
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```
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Ask: "Optimize my AWS setup to reduce costs by 30%. Current spend: $3000/month"
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Provide: Current resource inventory (EC2, RDS, S3, etc.)
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Result:
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- Idle resource identification
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- Right-sizing recommendations
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- Savings Plans analysis
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- Storage lifecycle policies
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- Target savings: $900/month
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```
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### IaC Generation
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```
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Ask: "Generate CloudFormation for a three-tier web app with auto-scaling"
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Result:
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- VPC with public/private subnets
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- ALB with HTTPS
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- ECS Fargate with auto-scaling
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- Aurora with read replicas
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- Security groups and IAM roles
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```
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---
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## Input Requirements
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Provide these details for architecture design:
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| Requirement | Description | Example |
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|-------------|-------------|---------|
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| Application type | What you're building | SaaS platform, mobile backend |
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| Expected scale | Users, requests/sec | 10k users, 100 RPS |
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| Budget | Monthly AWS limit | $500/month max |
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| Team context | Size, AWS experience | 3 devs, intermediate |
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| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |
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| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |
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**JSON Format:**
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```json
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{
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"application_type": "saas_platform",
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"expected_users": 10000,
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"requests_per_second": 100,
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"budget_monthly_usd": 500,
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"team_size": 3,
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"aws_experience": "intermediate",
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"compliance": ["SOC2"],
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"availability_sla": "99.9%"
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}
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```
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---
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## Output Formats
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### Architecture Design
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- Pattern recommendation with rationale
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- Service stack diagram (ASCII)
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- Configuration specifications
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- Monthly cost estimate
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- Scaling characteristics
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- Trade-offs and limitations
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### IaC Templates
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- **CloudFormation YAML**: Production-ready SAM/CFN templates
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- **CDK TypeScript**: Type-safe infrastructure code
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- **Terraform HCL**: Multi-cloud compatible configs
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### Cost Analysis
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- Current spend breakdown
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- Optimization recommendations with savings
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- Priority action list (high/medium/low)
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- Implementation checklist
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---
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## Reference Documentation
|
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| Document | Contents |
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|----------|----------|
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| `references/architecture_patterns.md` | 6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region |
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| `references/service_selection.md` | Decision matrices for compute, database, storage, messaging |
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| `references/best_practices.md` | Serverless design, cost optimization, security hardening, scalability |
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---
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## Limitations
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- Lambda: 15-minute execution, 10GB memory max
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- API Gateway: 29-second timeout, 10MB payload
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- DynamoDB: 400KB item size, eventually consistent by default
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- Regional availability varies by service
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- Some services have AWS-specific lock-in
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