Discover existing cloud resources using Terraform Search queries and bulk import them into Terraform management. Use when bringing unmanaged infrastructure under Terraform control, auditing cloud resources, or migrating to IaC.
npx skills add https://github.com/hashicorp/agent-skills --skill terraform-search-import
Discover existing cloud resources using declarative queries and generate configuration for bulk import into Terraform state.
References:
BEFORE starting, you MUST verify the target resource type is supported:
# Check what list resources are available
./scripts/list_resources.sh aws # Specific provider
./scripts/list_resources.sh # All configured providers
./scripts/list_resources.sh <provider>Note: The list of supported resources is rapidly expanding. Always verify current support before using manual import.
Before writing queries, verify the provider supports list resources for your target resource type.
Run the helper script to extract supported list resources from your provider:
# From a directory with provider configuration (runs terraform init if needed)
./scripts/list_resources.sh aws # Specific provider
./scripts/list_resources.sh # All configured providers
Or manually query the provider schema:
terraform providers schema -json | jq '.provider_schemas | to_entries | map({key: (.key | split("/")[-1]), value: (.value.list_resource_schemas // {} | keys)})'
Terraform Search requires an initialized working directory. Ensure you have a configuration with the required provider before running queries:
# terraform.tf
terraform {
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 6.0"
}
}
}
Run terraform init to download the provider, then proceed with queries.
.tfquery.hcl files with list blocks defining search queriesterraform query to discover matching resources-generate-config-out=<file>resource and import blocksterraform plan and terraform apply to importQuery files use .tfquery.hcl extension and support:
provider blocks for authenticationlist blocks for resource discoveryvariable and locals blocks for parameterization# discovery.tfquery.hcl
provider "aws" {
region = "us-west-2"
}
list "aws_instance" "all" {
provider = aws
}
list "<list_type>" "<symbolic_name>" {
provider = <provider_reference> # Required
# Optional: filter configuration (provider-specific)
# The `config` block schema is provider-specific. Discover available options using `terraform providers schema -json | jq '.provider_schemas."registry.terraform.io/hashicorp/<provider>".list_resource_schemas."<resource_type>"'`
config {
filter {
name = "<filter_name>"
values = ["<value1>", "<value2>"]
}
region = "<region>" # AWS-specific
}
# Optional: limit results
limit = 100
}
Provider support for list resources varies by version. Always check what's available for your specific provider version using the discovery script.
# Find all EC2 instances in configured region
list "aws_instance" "all" {
provider = aws
}
# Find instances by tag
list "aws_instance" "production" {
provider = aws
config {
filter {
name = "tag:Environment"
values = ["production"]
}
}
}
# Find instances by type
list "aws_instance" "large" {
provider = aws
config {
filter {
name = "instance-type"
values = ["t3.large", "t3.xlarge"]
}
}
}
provider "aws" {
region = "us-west-2"
}
locals {
regions = ["us-west-2", "us-east-1", "eu-west-1"]
}
list "aws_instance" "all_regions" {
for_each = toset(local.regions)
provider = aws
config {
region = each.value
}
}
variable "target_environment" {
type = string
default = "staging"
}
list "aws_instance" "by_env" {
provider = aws
config {
filter {
name = "tag:Environment"
values = [var.target_environment]
}
}
}
# Execute queries and display results
terraform query
# Generate configuration file
terraform query -generate-config-out=imported.tf
# Pass variables
terraform query -var='target_environment=production'
list.aws_instance.all account_id=123456789012,id=i-0abc123,region=us-west-2 web-server
Columns: <query_address> <identity_attributes> <name_tag>
The -generate-config-out flag creates:
# __generated__ by Terraform
resource "aws_instance" "all_0" {
ami = "ami-0c55b159cbfafe1f0"
instance_type = "t2.micro"
# ... all attributes
}
import {
to = aws_instance.all_0
provider = aws
identity = {
account_id = "123456789012"
id = "i-0abc123"
region = "us-west-2"
}
}
Generated configuration includes all attributes. Clean up by:
# Before: generated
resource "aws_instance" "all_0" {
ami = "ami-0c55b159cbfafe1f0"
instance_type = "t2.micro"
arn = "arn:aws:ec2:..." # Remove - computed
id = "i-0abc123" # Remove - computed
# ... many more attributes
}
# After: cleaned
resource "aws_instance" "web_server" {
ami = var.ami_id
instance_type = var.instance_type
subnet_id = var.subnet_id
tags = {
Name = "web-server"
Environment = var.environment
}
}
Generated imports use identity-based import (Terraform 1.12+):
import {
to = aws_instance.web
provider = aws
identity = {
account_id = "123456789012"
id = "i-0abc123"
region = "us-west-2"
}
}
limit to prevent overwhelming output| Issue | Solution |
|-------|----------|
| "No list resources found" | Check provider version supports list resources |
| Query returns empty | Verify region and filter values |
| Generated config has errors | Remove computed attributes, fix deprecated arguments |
| Import fails | Ensure resource not already in state |
# main.tf - Initialize provider
terraform {
required_version = ">= 1.14"
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 6.0" # Always use latest version
}
}
}
# discovery.tfquery.hcl - Define queries
provider "aws" {
region = "us-west-2"
}
list "aws_instance" "team_instances" {
provider = aws
config {
filter {
name = "tag:Owner"
values = ["platform"]
}
filter {
name = "instance-state-name"
values = ["running"]
}
}
limit = 50
}
# Execute workflow
terraform init
terraform query
terraform query -generate-config-out=generated.tf
# Review and clean generated.tf
terraform plan
terraform apply
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take hashicorp/terraform-search-import from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.