一键导入
terraform-iac-expert
Infrastructure as Code for AI workloads using Terraform across AWS, Azure, GCP, and OCI
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Infrastructure as Code for AI workloads using Terraform across AWS, Azure, GCP, and OCI
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | Terraform IaC Expert |
| description | Infrastructure as Code for AI workloads using Terraform across AWS, Azure, GCP, and OCI |
| version | 1.1.0 |
| last_updated | "2026-01-06T00:00:00.000Z" |
| external_version | Terraform 1.10+ |
| resources | resources/modules.tf |
| triggers | ["terraform","infrastructure as code","IaC","provisioning"] |
Expert in Terraform and Infrastructure as Code for deploying AI infrastructure across multi-cloud environments.
infrastructure/
├── modules/
│ ├── aws-bedrock/
│ ├── azure-openai/
│ ├── oci-genai/
│ └── vector-store/
├── environments/
│ ├── dev/
│ ├── staging/
│ └── prod/
├── shared/
│ ├── networking/
│ └── security/
└── scripts/
| Module | Provider | Purpose |
|---|---|---|
aws-bedrock | AWS | Bedrock, Knowledge Bases, VPC Endpoints |
azure-openai | Azure | Azure OpenAI, AI Search, Private Endpoints |
oci-genai | OCI | DACs, Endpoints, Agents, Knowledge Bases |
vector-store | Multi | OpenSearch Serverless, Qdrant, Milvus |
Full module code: resources/modules.tf
module "aws_ai" {
source = "./modules/aws-bedrock"
prefix = "prod"
region = "us-east-1"
vpc_id = data.aws_vpc.main.id
private_subnet_ids = data.aws_subnets.private.ids
enable_private_endpoint = true
create_knowledge_base = true
}
module "azure_ai" {
source = "./modules/azure-openai"
openai_name = "prod-openai"
location = "eastus"
resource_group_name = azurerm_resource_group.ai.name
gpt4o_capacity = 100 # TPM
embedding_capacity = 50
enable_private_endpoint = true
}
module "oci_ai" {
source = "./modules/oci-genai"
prefix = "prod"
compartment_id = var.oci_compartment_id
cluster_type = "HOSTING"
unit_count = 10
unit_shape = "LARGE_COHERE"
create_agent = true
create_knowledge_base = true
}
module "vectors" {
source = "./modules/vector-store/aws-opensearch"
prefix = "prod"
vpc_endpoint_ids = [aws_vpc_endpoint.opensearch.id]
allowed_principals = [aws_iam_role.bedrock.arn]
}
# environments/prod/main.tf
terraform {
backend "s3" {
bucket = "terraform-state-ai-infra"
key = "prod/terraform.tfstate"
encrypt = true
}
}
provider "aws" { region = var.aws_region }
provider "azurerm" { features {} }
provider "oci" { ... }
# Deploy to all clouds
module "aws_ai" { source = "../../modules/aws-bedrock" ... }
module "azure_ai" { source = "../../modules/azure-openai" ... }
module "oci_ai" { source = "../../modules/oci-genai" ... }
variable "environment" {
type = string
validation {
condition = contains(["dev", "staging", "prod"], var.environment)
error_message = "Environment must be dev, staging, or prod."
}
}
sensitive = true for outputs.tfvars with secretsdefault_tags {
tags = {
Environment = var.environment
Project = "ai-platform"
ManagedBy = "terraform"
}
}
- uses: hashicorp/setup-terraform@v3
- run: terraform init
- run: terraform plan -out=tfplan
- run: terraform apply -auto-approve tfplan
if: github.ref == 'refs/heads/main'
eks_managed_node_groups = {
gpu = {
instance_types = ["g5.2xlarge"]
ami_type = "AL2_x86_64_GPU"
taints = [{ key = "nvidia.com/gpu" ... }]
}
}
resource "azurerm_kubernetes_cluster_node_pool" "gpu" {
vm_size = "Standard_NC24ads_A100_v4"
node_taints = ["nvidia.com/gpu=true:NoSchedule"]
}
resource "oci_containerengine_node_pool" "gpu" {
node_shape = "BM.GPU.A100-v2.8"
}
Full examples: resources/modules.tf
Infrastructure as Code for enterprise AI deployments.
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.
Enterprise AI security - OWASP LLM Top 10, prompt injection defense, guardrails, PII protection
Create professional architecture diagrams using D2, Draw.io, Mermaid, and OCI official icons for enterprise-grade visualizations
Build AI applications on AWS using Bedrock, SageMaker, and AI/ML services with best practices for enterprise deployment
Build AI applications on Azure using Azure OpenAI, Cognitive Services, and ML services with enterprise patterns
Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices