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Aws Solution Architect

Community
yezannnnn
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. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.

Overview

Publisheryezannnnn
RepositoryagentGroup
Skill nameaws-solution-architect
Stars
149
Forks
49
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yezannnnn on GitHub. Read the source before you install it.

Installation

Install the Aws Solution Architect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yezannnnn/agentGroup.git /tmp/agentGroup
mkdir -p .claude/skills
cp -r /tmp/agentGroup/jarvis/skills/engineering-team/aws-solution-architect .claude/skills/aws-solution-architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Solution Architect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Solution Architect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Solution Architect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS Solution Architect

Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.


Table of Contents


Trigger Terms

Use this skill when you encounter:

CategoryTerms
Architecture Designserverless architecture, AWS architecture, cloud design, microservices, three-tier
IaC GenerationCloudFormation, CDK, Terraform, infrastructure as code, deploy template
ServerlessLambda, API Gateway, DynamoDB, Step Functions, EventBridge, AppSync
ContainersECS, Fargate, EKS, container orchestration, Docker on AWS
Cost Optimizationreduce AWS costs, optimize spending, right-sizing, Savings Plans
DatabaseAurora, RDS, DynamoDB design, database migration, data modeling
SecurityIAM policies, VPC design, encryption, Cognito, WAF
CI/CDCodePipeline, CodeBuild, CodeDeploy, GitHub Actions AWS
MonitoringCloudWatch, X-Ray, observability, alarms, dashboards
Migrationmigrate to AWS, lift and shift, replatform, DMS

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

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.

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

# Output: CloudFormation YAML template ready to deploy

Step 4: Review Costs

Analyze estimated costs and optimization opportunities:

bash
python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

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

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 ...

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:

RequirementDescriptionExample
Application typeWhat you're buildingSaaS platform, mobile backend
Expected scaleUsers, requests/sec10k users, 100 RPS
BudgetMonthly AWS limit$500/month max
Team contextSize, AWS experience3 devs, intermediate
ComplianceRegulatory needsHIPAA, GDPR, SOC 2
AvailabilityUptime requirements99.9% 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)
  • Configuration specifications
  • Monthly cost estimate
  • Scaling characteristics
  • Trade-offs and limitations

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
  • Optimization recommendations with savings
  • Priority action list (high/medium/low)
  • Implementation checklist

Reference Documentation

DocumentContents
references/architecture_patterns.md6 patterns: serverless, microservices, three-tier, data processing, GraphQL, multi-region
references/service_selection.mdDecision matrices for compute, database, storage, messaging
references/best_practices.mdServerless design, cost optimization, security hardening, scalability

Limitations

  • Lambda: 15-minute execution, 10GB memory max
  • API Gateway: 29-second timeout, 10MB payload
  • DynamoDB: 400KB item size, eventually consistent by default
  • Regional availability varies by service
  • Some services have AWS-specific lock-in

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Solution Architect AI skill do?

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.

Why use Aws Solution Architect on TypingMind?

Because you install it once and use it with any model. Aws Solution Architect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Solution Architect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yezannnnn/agentGroup/tree/master/jarvis/skills/engineering-team/aws-solution-architect. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Solution Architect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Solution Architect?

As many as you like. As long as a model supports skills, you can use Aws Solution Architect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Solution Architect AI skill free?

Yes. It is published on GitHub by yezannnnn under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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