infrastructure/cloud-aws/terraform-aws/SKILL.md
Provision AWS infrastructure with Terraform. Create modules, manage state, and implement IaC best practices. Use when deploying AWS resources declaratively.
npx skillsauth add bagelhole/devops-security-agent-skills terraform-awsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Provision and manage AWS infrastructure with Terraform.
terraform {
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
}
backend "s3" {
bucket = "terraform-state"
key = "prod/terraform.tfstate"
region = "us-east-1"
}
}
provider "aws" {
region = var.region
default_tags {
tags = {
Environment = var.environment
ManagedBy = "terraform"
}
}
}
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
enable_dns_hostnames = true
tags = { Name = "main-vpc" }
}
resource "aws_instance" "web" {
ami = data.aws_ami.amazon_linux.id
instance_type = "t3.micro"
subnet_id = aws_subnet.public.id
tags = { Name = "web-server" }
}
module "vpc" {
source = "terraform-aws-modules/vpc/aws"
name = "my-vpc"
cidr = "10.0.0.0/16"
azs = ["us-east-1a", "us-east-1b"]
private_subnets = ["10.0.1.0/24", "10.0.2.0/24"]
public_subnets = ["10.0.101.0/24", "10.0.102.0/24"]
enable_nat_gateway = true
}
terraform init
terraform plan -out=plan.tfplan
terraform apply plan.tfplan
terraform destroy
development
Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
testing
Harden OpenClaw self-hosted environments with baseline host controls, auth tightening, secret handling, network segmentation, and safe update/rollback workflows. Use when deploying OpenClaw in home labs, startups, or production-like local AI infrastructure.
devops
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
testing
Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.