Terraform Search Import logo

Terraform Search Import

Organization
hashicorp
terraform-search-import

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.

Overview

Publisherhashicorp
Repositoryagent-skills
Skill nameterraform-search-import
Stars
880
Forks
130
Bundled files
2
LicenseMPL-2.0
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 hashicorp on GitHub. Read the source before you install it.

Installation

Install the Terraform Search Import 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.
    https://github.com/hashicorp/agent-skills/tree/main/plugins/terraform/skills/terraform-search-import
  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/hashicorp/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/plugins/terraform/skills/terraform-search-import .claude/skills/terraform-search-import
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Terraform Search Import 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 Terraform Search Import 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 Terraform Search Import 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.

Terraform Search and Bulk Import

Discover existing cloud resources using declarative queries and generate configuration for bulk import into Terraform state.

References:

When to Use

  • Bringing unmanaged resources under Terraform control
  • Auditing existing cloud infrastructure
  • Migrating from manual provisioning to IaC
  • Discovering resources across multiple regions/accounts

IMPORTANT: Check Provider Support First

BEFORE starting, you MUST verify the target resource type is supported:

bash
# Check what list resources are available
./scripts/list_resources.sh aws      # Specific provider
./scripts/list_resources.sh          # All configured providers

Decision Tree

  1. Identify target resource type (e.g., aws_s3_bucket, aws_instance)

  2. Check if supported: Run ./scripts/list_resources.sh <provider>

  3. Choose workflow:

    • ** If supported**: Check for terraform version available.
    • ** If terraform version is above 1.14.0** Use Terraform Search workflow (below)
    • ** If not supported or terraform version is below 1.14.0 **: Use Manual Discovery workflow (see references/MANUAL-IMPORT.md)

    Note: The list of supported resources is rapidly expanding. Always verify current support before using manual import.

Prerequisites

Before writing queries, verify the provider supports list resources for your target resource type.

Discover Available List Resources

Run the helper script to extract supported list resources from your provider:

bash
# 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:

bash
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:

hcl
# terraform.tf
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 6.0"
    }
  }
}

Run terraform init to download the provider, then proceed with queries.

Terraform Search Workflow (Supported Resources Only)

  1. Create .tfquery.hcl files with list blocks defining search queries
  2. Run terraform query to discover matching resources
  3. Generate configuration with -generate-config-out=<file>
  4. Review and refine generated resource and import blocks
  5. Run terraform plan and terraform apply to import

Query File Structure

Query files use .tfquery.hcl extension and support:

  • provider blocks for authentication
  • list blocks for resource discovery
  • variable and locals blocks for parameterization
hcl
# discovery.tfquery.hcl
provider "aws" {
  region = "us-west-2"
}

list "aws_instance" "all" {
  provider = aws
}

List Block Syntax

hcl
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
}

Supported List Resources

Provider support for list resources varies by version. Always check what's available for your specific provider version using the discovery script.

Query Examples

Basic Discovery

hcl
# Find all EC2 instances in configured region
list "aws_instance" "all" {
  provider = aws
}

Filtered Discovery

hcl
# 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"]
    }
  }
}

Multi-Region Discovery

hcl
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
  }
}

Parameterized Queries

hcl
variable "target_environment" {
  type    = string
  default = "staging"
}

list "aws_instance" "by_env" {
  provider = aws

  config {
    filter {
      name   = "tag:Environment"
      values = [var.target_environment]
    }
  }
}

Running Queries

bash
# 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'

Query Output Format

list.aws_instance.all   account_id=123456789012,id=i-0abc123,region=us-west-2   web-server

Columns: <query_address> <identity_attributes> <name_tag>

Generated Configuration

The -generate-config-out flag creates:

hcl
# __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"
  }
}

Post-Generation Cleanup

Generated configuration includes all attributes. Clean up by:

  1. Remove computed/read-only attributes
  2. Replace hardcoded values with variables
  3. Add proper resource naming
  4. Organize into appropriate files
hcl
# 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
  }
}

Import by Identity

Generated imports use identity-based import (Terraform 1.12+):

hcl
import {
  to       = aws_instance.web
  provider = aws
  identity = {
    account_id = "123456789012"
    id         = "i-0abc123"
    region     = "us-west-2"
  }
}

Verifying Imported State

After running terraform apply to import resources, verify what actually landed in state. Prefer the documented, stable, and more token-efficient commands over reading the raw state file:

bash
# Confirm resources are now managed (also confirms addresses)
terraform state list

# Inspect resolved attribute values for imported resources
terraform show -json | jq '.values.root_module.resources[] | {address, type, name}'

terraform show -json requires providers to be installed (terraform init), since it renders values against provider schemas. Fall back to the raw state (terraform state pull / terraform.tfstate) only when providers aren't available and init can't run, you need only coarse info (addresses, outputs, serial/lineage), or you must avoid executing Terraform. Avoid parsing the raw version-4 state format as a stable interface. Note: state contains sensitive values in plaintext in every format — never echo state contents into logs or output.

Best Practices

Query Design

  • Start broad, then add filters to narrow results
  • Use limit to prevent overwhelming output
  • Test queries before generating configuration

Configuration Management

  • Review all generated code before applying
  • Remove unnecessary default values
  • Use consistent naming conventions
  • Add proper variable abstraction

Troubleshooting

IssueSolution
"No list resources found"Check provider version supports list resources
Query returns emptyVerify region and filter values
Generated config has errorsRemove computed attributes, fix deprecated arguments
Import failsEnsure resource not already in state

Complete Example

hcl
# 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
}
bash
# Execute workflow
terraform init
terraform query
terraform query -generate-config-out=generated.tf
# Review and clean generated.tf
terraform plan
terraform apply

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 Terraform Search Import AI skill do?

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.

Why use Terraform Search Import on TypingMind?

Because you install it once and use it with any model. Terraform Search Import 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 Terraform Search Import in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hashicorp/agent-skills/tree/main/plugins/terraform/skills/terraform-search-import. 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 Terraform Search Import?

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 Terraform Search Import?

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

Is the Terraform Search Import AI skill free?

Yes. It is published on GitHub by hashicorp under the MPL-2.0 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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