Aws solution architect

Design AWS architectures for startups using serverless patterns and IaC templates.

How to use it

Claude Code
  1. Run the line below. It pulls the whole folder into ~/.claude/skills/aws-solution-architect, including the files SKILL.md points to.
  2. Describe your job in plain words. Claude Code follows the skill from there.
Claude Code — installs the whole folder, not just SKILL.md
npx degit alirezarezvani/claude-skills/engineering-team/skills/aws-solution-architect#main ~/.claude/skills/aws-solution-architect

For one project only, change the path to .claude/skills/aws-solution-architect. This skill also uses requirements.json, serverless_stack.py, current_setup.json, architecture_designer.py, design.json, cost_optimizer.py — copying SKILL.md alone won't be enough. See the folder on GitHub.

Claude (web or desktop app)
  1. On this page open ⋯ → Download .md.
  2. Save it as SKILL.md in a folder, zip the folder, then Customize → Skills → + → Create skill → Upload a skill.
  3. Pick the file and Save. Claude shows the name and description and runs a security scan.
  4. Check the skill is switched on.
  5. Start a new chat and describe your job in plain words. The AI follows the skill from there.
ChatGPT or another app
  1. ChatGPT: make a Project and paste it into Instructions.
  2. Neither? Paste it at the top of a new chat — it works for that chat.
Not working?
  • Check which app you pasted it into — the steps above name the right one.
  • Some skills need the paid tier of Claude or ChatGPT.
Step-by-step guide with screenshots · Ask in the forum

Paste into Claude, ChatGPT or Cursor.

Source of Aws solution architect

Show the full text382 lines
namedescription
aws-solution-architectDesign 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:

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.py and also available in references/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:

  1. Check the failure reason:
    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:
    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.

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---
2name: "aws-solution-architect"
3description: 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 
8Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.
9 
10---
11 
12## Workflow
13 
14### Step 1: Gather Requirements
15 
16Collect 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 
29Run the architecture designer to get pattern recommendations:
30 
31```bash
32python scripts/architecture_designer.py --input requirements.json
33```
34 
35**Example output:**
36 
37```json
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 
47Select 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 
53See `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 
59Create infrastructure-as-code for the selected pattern:
60 
61```bash
62# Serverless stack (CloudFormation)
63python scripts/serverless_stack.py --app-name my-app --region us-east-1
64```
65 
66**Example CloudFormation YAML output (core serverless resources):**
67 
68```yaml
69AWSTemplateFormatVersion: '2010-09-09'
70Transform: AWS::Serverless-2016-10-31
71 
72Parameters:
73 AppName:
74 Type: String
75 Default: my-app
76 
77Resources:
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```typescript
119import * as ecs from 'aws-cdk-lib/aws-ecs';
120import * as ec2 from 'aws-cdk-lib/aws-ec2';
121import * as rds from 'aws-cdk-lib/aws-rds';
122 
123const vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 });
124 
125const cluster = new ecs.Cluster(this, 'AppCluster', { vpc });
126 
127const 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 
138Analyze estimated costs and optimization opportunities:
139 
140```bash
141python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000
142```
143 
144**Example output:**
145 
146```json
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 
158Output 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 
166Deploy the generated infrastructure:
167 
168```bash
169# CloudFormation
170aws cloudformation create-stack \
171 --stack-name my-app-stack \
172 --template-body file://template.yaml \
173 --capabilities CAPABILITY_IAM
174 
175# CDK
176cdk deploy
177 
178# Terraform
179terraform init && terraform apply
180```
181 
182### Step 6: Validate and Handle Failures
183 
184Verify deployment and set up monitoring:
185 
186```bash
187# Check stack status
188aws cloudformation describe-stacks --stack-name my-app-stack
189 
190# Set up CloudWatch alarms
191aws cloudwatch put-metric-alarm --alarm-name high-errors ...
192```
193 
194**If stack creation fails:**
195 
1961. Check the failure reason:
197 ```bash
198 aws cloudformation describe-stack-events \
199 --stack-name my-app-stack \
200 --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'
201 ```
2022. Review CloudWatch Logs for Lambda or ECS errors.
2033. Fix the template or resource configuration.
2044. Delete the failed stack before retrying:
205 ```bash
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 
224Generates architecture patterns based on requirements.
225 
226```bash
227python 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 
235Creates serverless CloudFormation templates.
236 
237```bash
238python 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 
250Analyzes costs and recommends optimizations.
251 
252```bash
253python 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```
270Ask: "Design a serverless MVP backend for a mobile app with 1000 users"
271 
272Result:
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```
283Ask: "Design a scalable architecture for a SaaS platform with 50k users"
284 
285Result:
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```
297Ask: "Optimize my AWS setup to reduce costs by 30%. Current spend: $3000/month"
298 
299Provide: Current resource inventory (EC2, RDS, S3, etc.)
300 
301Result:
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```
312Ask: "Generate CloudFormation for a three-tier web app with auto-scaling"
313 
314Result:
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 
326Provide 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```json
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 

Discussion

Alternatives

Also in Cloud & infraSee all 533 in Development →
Docker MCP gatewayDocker's own CLI plugin: run any server from the Docker MCP Catalog in its own container, behind one connection, with secrets kept out of env vars.Coding · MITTechnical Codebase Discovery & Onboarding PromptA prompt designed to guide a deep technical analysis of a code repository to accelerate developer onboarding. It instructs an AI to analyze the entire codebase and generate a structured Markdown document covering architecture, technology stack, key components, execution and data flows, integrations, testing, security, and build/deployment, serving as a technical reference guide.Coding · CC0-1.0NextflowBuild, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, samplesheets, or wants to run a community pipeline (e.g. nf-core/rnaseq, nf-core/sarek), write or test a module/subworkflow with nf-test, configure executors/containers (Docker, Singularity/Apptainer, Conda, Wave), scale a workflow to HPC/SLURM or cloud (AWS Batch, Google Batch, Azure, Kubernetes), or debug a failed/-resume run. Make sure to use this skill for any reproducible scientific/bioinformatics workflow work even if the user does not say the word "Nextflow", and for authoring nf-core-compliant pipelines, modules, configs, and linting.Science · MITCloud Cost OptimizationOptimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.Infrastructure & ops · MIT