terraform-iac

name

terraform-iac

description

Create, modify, plan, apply, and destroy AWS and Azure infrastructure using Terraform Cloud. Use when asked to provision cloud resources, manage IaC, write Terraform configs, run terraform plan/apply/destroy, manage workspaces, or maintain infrastructure state. Triggers on phrases like "create an S3 bucket", "provision a VNet", "deploy infrastructure", "terraform plan", "apply infra", "destroy resources", "show current state", "what's deployed".

Terraform IaC Agent

Manages AWS and Azure infrastructure via Terraform Cloud (TFC). All state lives in TFC — no local state files.

Configuration

Required env vars (set once in OpenClaw config):

• TFC_TOKEN — Terraform Cloud user API token or team API token (not an organization token — org tokens lack permissions for plan/apply operations). Generate at: User Settings → Tokens, or Organization → Teams → Team API Token.
• TFC_ORG — Terraform Cloud organization name
• TFC_WORKSPACE_AWS — workspace name for AWS resources (e.g. prod-aws)
• TFC_WORKSPACE_AZURE — workspace name for Azure resources (e.g. prod-azure)

AWS provider credentials set as TFC workspace variables:

• AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION

Azure provider credentials set as TFC workspace variables:

• ARM_CLIENT_ID, ARM_CLIENT_SECRET, ARM_SUBSCRIPTION_ID, ARM_TENANT_ID

Workflow

User request → Generate/modify .tf files → Run plan → Show output → Wait for approval → Apply

Always show plan and wait for explicit approval before applying.

Step 1: Understand the request

Identify:

• Cloud target: AWS, Azure, or both
• Resource type and configuration
• Whether this is create / modify / destroy

Step 2: Generate Terraform config

Use {baseDir}/scripts/tfc_client.py generate with the appropriate resource type:

python3 {baseDir}/scripts/tfc_client.py generate \
  --resource s3 --name my-bucket \
  --workspace prod-aws --dir /tmp/tf-aws

Supported resources: s3, vpc, ec2, sg, lambda, iam-user, iam-role, budget, cloudtrail, cloudwatch, efs, landing-zone, rg

Interactive wizards (vpc, ec2, sg, lambda, iam-user, budget, cloudtrail, cloudwatch, efs, landing-zone) will prompt for configuration with design guidance.

See {baseDir}/references/aws.md and {baseDir}/references/azure.md for provider patterns.

Step 3: Run plan

python3 {baseDir}/scripts/tfc_client.py plan --dir /tmp/tf-aws

Show the plan output to the user. Summarize what will be created/changed/destroyed.

Step 4: Wait for approval

Ask: "The plan looks like X. Shall I apply? (yes/no)"

Do NOT apply without explicit confirmation.

Step 5: Apply or abort

# Apply
python3 {baseDir}/scripts/tfc_client.py apply --dir /tmp/tf-aws

# Destroy (requires separate confirmation)
python3 {baseDir}/scripts/tfc_client.py destroy --dir /tmp/tf-aws

Step 6: Show outputs

After apply, fetch and display outputs:

python3 {baseDir}/scripts/tfc_client.py outputs --workspace prod-aws

State Queries

To answer "what's currently deployed":

python3 {baseDir}/scripts/tfc_client.py state --workspace prod-aws

Workspace Management

python3 {baseDir}/scripts/tfc_client.py list-workspaces

Rules

• Never apply without showing plan first and getting explicit approval
• Never store credentials in .tf files — always use TFC workspace variables
• Destroy requires a second explicit confirmation: "Type DESTROY to confirm"
• Keep AWS and Azure in separate TFC workspaces
• Always pin provider versions in required_providers