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Aws Cloudformation Vpc

Community
giuseppe-trisciuoglio
aws-cloudformation-vpc

Provides AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application deployments.

Overview

Publishergiuseppe-trisciuoglio
Repositorydeveloper-kit
Skill nameaws-cloudformation-vpc
Stars
345
Forks
41
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by giuseppe-trisciuoglio on GitHub. Read the source before you install it.

Installation

Install the Aws Cloudformation Vpc 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/giuseppe-trisciuoglio/developer-kit.git /tmp/developer-kit
mkdir -p .claude/skills
cp -r /tmp/developer-kit/plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-vpc .claude/skills/aws-cloudformation-vpc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Cloudformation Vpc 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 Cloudformation Vpc 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 Cloudformation Vpc 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 CloudFormation VPC Infrastructure

Overview

Build a VPC foundation with CloudFormation that stays readable, reusable, and safe to evolve. Provides a clear subnet and routing model with predictable connectivity for public and private workloads, plus outputs that downstream stacks can consume without duplicating network logic.

Use the references/ files for larger templates and extended service combinations.

When to Use

  • Creating a new VPC stack for an application or shared platform
  • Adding public and private subnets across one or more Availability Zones
  • Wiring internet access, NAT egress, or private endpoints
  • Exporting VPC, subnet, route table, and security-group-adjacent identifiers for other stacks
  • Preparing reusable infrastructure for ECS, EKS, Lambda, EC2, or RDS stacks

Instructions

1. Start with the address plan

Before writing resources, define:

  • VPC CIDR range
  • Number of Availability Zones
  • Public, private, and isolated subnet ranges
  • Which workloads need internet ingress, NAT egress, or only private AWS service access

This prevents route-table sprawl and painful subnet replacement later.

2. Build the core network resources in layers

Create the stack in this order:

  1. VPC and subnets
  2. Internet Gateway for public ingress and egress
  3. NAT gateways if private subnets need outbound internet access
  4. Route tables and subnet associations
  5. Optional VPC endpoints for private access to AWS services

Keep each layer easy to inspect in the template and avoid mixing unrelated application resources into the same stack.

3. Parameterize only the environment-dependent values

Useful parameters include:

  • Environment name
  • VPC CIDR and subnet CIDRs
  • Number of AZs or explicit subnet IDs in nested-stack scenarios
  • Flags for optional endpoints or NAT layout

Do not parameterize every route or tag unless it meaningfully changes between environments.

4. Export only what consumers really need

Typical outputs:

  • VPC ID
  • Public, private, and isolated subnet IDs
  • Route table IDs when downstream stacks must attach routes
  • Security boundaries or prefix-list references only when another stack consumes them

Stable outputs make application stacks easier to compose and migrate.

5. Validate before deployment

Run these commands to validate the template and verify routing:

bash
# Validate CloudFormation template syntax
aws cloudformation validate-template --template-body file://vpc.yaml

# Review change set before applying
aws cloudformation create-change-set \
  --stack-name my-vpc \
  --template-body file://vpc.yaml \
  --change-set-type CREATE

# Verify route table associations
aws ec2 describe-route-tables \
  --filters "Name=vpc-id,Values=<vpc-id>"

# Check subnet to route table mappings
aws ec2 describe-route-tables \
  --filters "Name=association.subnet-id,Values=<subnet-id>"

# Verify internet gateway attachment
aws ec2 describe-internet-gateways \
  --filters "Name=attachment.vpc-id,Values=<vpc-id>"

Examples

Example 1: Complete two-tier VPC with routing

This template creates a VPC with public and private subnets, internet gateway, NAT gateway, and properly configured route tables.

yaml
AWSTemplateFormatVersion: "2010-09-09"
Description: "Two-tier VPC with public and private subnets"

Resources:
  # VPC
  MainVpc:
    Type: AWS::EC2::VPC
    Properties:
      CidrBlock: 10.0.0.0/16
      EnableDnsHostnames: true
      EnableDnsSupport: true
      Tags:
        - Key: Name
          Value: !Sub "${AWS::StackName}-main"

  # Internet Gateway
  InternetGateway:
    Type: AWS::EC2::InternetGateway
    Properties:
      Tags:
        - Key: Name
          Value: !Sub "${AWS::StackName}-igw"

  # Attach IGW to VPC
  GatewayToInternet:
    Type: AWS::EC2::VPCGatewayAttachment
    Properties:
      VpcId: !Ref MainVpc
      InternetGatewayId: !Ref InternetGateway

  # Public Subnet (AZ 1)
  PublicSubnetA:
    Type: AWS::EC2::Subnet
    Properties:
      VpcId: !Ref MainVpc
      CidrBlock: 10.0.1.0/24
      AvailabilityZone: !Select [0, !GetAZs ""]
      MapPublicIpOnLaunch: true
      Tags:
        - Key: Name
          Value: !Sub "${AWS::StackName}-public-a"

  # Private Subnet (AZ 1)
  PrivateSubnetA:
    Type: AWS::EC2::Subnet
    Properties:
      VpcId: !Ref MainVpc
      CidrBlock: 10.0.11.0/24
      AvailabilityZone: !Select [0, !GetAZs ""]
      Tags:
        - Key: Name
          Value: !Sub "${AWS::StackName}-private-a"

  # Elastic IP for NAT Gateway
  NatEip:
    Type: AWS::EC2::EIP
    DependsOn: GatewayToInternet
    Properties:
      Domain: vpc

  # NAT Gateway
  NatGateway:
    Type: AWS::EC2::NatGateway
    Properties:
      SubnetId: !Ref PublicSubnetA
      AllocationId: !GetAtt NatEip.AllocationId

  # Public Route Table
  PublicRouteTable:
    Type: AWS::EC2::RouteTable
    Properties:
      VpcId: !Ref MainVpc
      Tags:
        - Key: Name
          Value: !Sub "${AWS::StackName}-public-rt"

  # Default route to IGW
  PublicDefaultRoute:
    Type: AWS::EC2::Route
    DependsOn: GatewayToInternet
    Properties:
      RouteTableId: !Ref PublicRouteTable
      DestinationCidrBlock: 0.0.0.0/0
      GatewayId: !Ref InternetGateway

  # Associate public subnet
  PublicSubnetARouteTableAssociation:
    Type: AWS::EC2::SubnetRouteTableAssociation
    Properties:
      SubnetId: !Ref PublicSubnetA
      RouteTableId: !Ref PublicRouteTable

  # Private Route Table
  PrivateRouteTable:
    Type: AWS::EC2::RouteTable
    Properties:
      VpcId: !Ref MainVpc
      Tags:
        - Key: Name
          Value: !Sub "${AWS::StackName}-private-rt"

  # Default route via NAT Gateway
  PrivateDefaultRoute:
    Type: AWS::EC2::Route
    Properties:
      RouteTableId: !Ref PrivateRouteTable
      DestinationCidrBlock: 0.0.0.0/0
      NatGatewayId: !Ref NatGateway

  # Associate private subnet
  PrivateSubnetARouteTableAssociation:
    Type: AWS::EC2::SubnetRouteTableAssociation
    Properties:
      SubnetId: !Ref PrivateSubnetA
      RouteTableId: !Ref PrivateRouteTable

Outputs:
  VpcId:
    Description: VPC ID
    Value: !Ref MainVpc
    Export:
      Name: !Sub "${AWS::StackName}-VpcId"

  PublicSubnetA:
    Description: Public subnet AZ1
    Value: !Ref PublicSubnetA
    Export:
      Name: !Sub "${AWS::StackName}-PublicSubnetA"

  PrivateSubnetA:
    Description: Private subnet AZ1
    Value: !Ref PrivateSubnetA
    Export:
      Name: !Sub "${AWS::StackName}-PrivateSubnetA"

  PublicRouteTableId:
    Description: Public route table ID
    Value: !Ref PublicRouteTable
    Export:
      Name: !Sub "${AWS::StackName}-PublicRouteTableId"

  PrivateRouteTableId:
    Description: Private route table ID
    Value: !Ref PrivateRouteTable
    Export:
      Name: !Sub "${AWS::StackName}-PrivateRouteTableId"

Example 2: VPC endpoint for private S3 access

yaml
Resources:
  # S3 VPC Endpoint
  S3Endpoint:
    Type: AWS::EC2::VPCEndpoint
    Properties:
      VpcId: !Ref MainVpc
      ServiceName: !Sub "com.amazonaws.${AWS::Region}.s3"
      RouteTableIds:
        - !Ref PrivateRouteTable
      VpcEndpointType: Gateway

Best Practices

  • Keep public, private, and isolated subnet purposes explicit in names and tags
  • Prefer one NAT gateway per AZ for resilient production environments when budget allows
  • Use VPC endpoints to reduce unnecessary NAT traffic for AWS service access
  • Export VPC and subnet identifiers from the network stack instead of recreating network assumptions elsewhere
  • Review network changes with dependency stacks because route and subnet changes can have broad blast radius
  • Keep the root skill focused and move larger networking variants to references/examples.md

Constraints and Warnings

  • NAT gateways incur hourly costs and data transfer charges—consider VPC endpoints for AWS service access
  • CIDR overlap blocks peering, transit, and future network expansion
  • Route-table or subnet replacements can interrupt traffic even when the template is valid
  • Endpoint quotas, AZ availability, and service-specific subnet requirements vary by region
  • Hardcoding Availability Zones can reduce portability across accounts and regions

References

  • references/examples.md
  • references/reference.md

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 Cloudformation Vpc AI skill do?

Provides AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application deployments.

Why use Aws Cloudformation Vpc on TypingMind?

Because you install it once and use it with any model. Aws Cloudformation Vpc 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 Cloudformation Vpc in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/giuseppe-trisciuoglio/developer-kit/tree/main/plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-vpc. 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 Cloudformation Vpc?

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 Cloudformation Vpc?

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

Is the Aws Cloudformation Vpc AI skill free?

Yes. It is published on GitHub by giuseppe-trisciuoglio 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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