Infrastructure-as-code specialist for multi-cloud provisioning using Terraform across any provider (AWS, GCP, Azure, Oracle Cloud). Use for terraform plan/apply, state management, compute, databases, storage, networking, IAM, OIDC, cost optimization, policy-as-code, ISO/IEC 42001 AI controls, ISO 22301 continuity, and ISO/IEC/IEEE 42010 architecture documentation.
npx skills add https://github.com/first-fluke/oh-my-agent --skill oma-tf-infra
Design, implement, review, and document Terraform-based infrastructure across cloud providers with secure state, least privilege, cost awareness, continuity, and policy/testing controls.
.tf, .tfvars, modules, provider versions, CI/CD auth, plan output, or drift symptomsresources/multi-cloud-examples.md, cost guide, policy/testing examples, ISO infra guide, and checklist| Action | SSL primitive | Evidence |
|--------|---------------|----------|
| Detect provider and scope | READ | HCL, providers, modules |
| Select cloud/resource mapping | SELECT | Multi-cloud mapping |
| Write Terraform | WRITE | .tf, .tfvars, modules |
| Validate HCL | CALL_TOOL | terraform fmt, validate, plan |
| Compare plan risk | COMPARE | Plan output and drift |
| Infer cost/security/continuity risks | INFER | Policy, ISO, cost guides |
| Report result | NOTIFY | Final infra summary |
terraform fmt -recursive
terraform validate
terraform plan -out=tfplan
Run scanners when available before any apply:
checkov -d .
tfsec .
| Scope | Resource target |
|-------|-----------------|
| CODEBASE | Terraform modules, variables, outputs, CI config |
| LOCAL_FS | Plans, state config, documentation |
| PROCESS | Terraform, scanner, and policy commands |
| CREDENTIALS | Cloud provider auth and state backend credentials |
| NETWORK | Cloud APIs and remote state backends |
terraform validate, terraform fmt, terraform plan before apply10. Policy as Code: Run OPA/Sentinel and security scanning (Checkov, tfsec) in CI/CD before apply
11. Version Pinning: Version pin all providers and modules; use for_each over count (never count with computed values)
12. Cost Awareness: Implement lifecycle policies, autoscaling schedules, and review cost estimates before apply
13. No Auto-Approve: Never use auto-approve in production; never terraform destroy without backup/confirmation
14. Drift Detection: Never skip drift detection in production; address deprecation warnings from providers
15. AI Systems: Document IAM, logging, encryption, monitoring, and retention controls; prefer private connectivity; limit to infrastructure controls (note when policy/process work belongs elsewhere)
16. Continuity: Document backup, failover, dependency visibility, and restore validation with target RTO/RPO (not backup-only)
17. Architecture Documentation: Capture stakeholders, concerns, views, interfaces, constraints, and decisions (not a compliance checkbox; improve communication and traceability)
| Indicator | Provider |
|-----------|----------|
| provider "google" or google_* resources | GCP |
| provider "aws" or aws_* resources | AWS |
| provider "azurerm" or azurerm_* resources | Azure |
| provider "oci" or oci_* resources | Oracle Cloud |
| Concept | AWS | GCP | Azure | Oracle (OCI) |
|---------|-----|-----|-------|--------------|
| Container Platform | ECS Fargate | Cloud Run | Container Apps | Container Instances |
| Managed Kubernetes | EKS | GKE | AKS | OKE |
| Managed Database | RDS | Cloud SQL | Azure SQL | Autonomous DB |
| Cache/In-Memory | ElastiCache | Memorystore | Azure Cache | OCI Cache |
| Object Storage | S3 | GCS | Blob Storage | Object Storage |
| Queue/Messaging | SQS/SNS | Pub/Sub | Service Bus | OCI Streaming |
| Task Queue | N/A | Cloud Tasks | Queue Storage | N/A |
| CDN | CloudFront | Cloud CDN | Front Door | OCI CDN |
| Load Balancer | ALB/NLB | Cloud Load Balancing | Load Balancer | OCI Load Balancer |
| IAM Role | IAM Role | Service Account | Managed Identity | Dynamic Group |
| Secrets | Secrets Manager | Secret Manager | Key Vault | OCI Vault |
| VPC | VPC | VPC | Virtual Network | VCN |
| Serverless Function | Lambda | Cloud Functions | Functions | OCI Functions |
Follow resources/execution-protocol.md step by step.
See resources/examples.md for input/output examples.
Use resources/multi-cloud-examples.md for provider-specific HCL patterns.
Use resources/cost-optimization.md for cost reduction strategies.
Use resources/policy-testing-examples.md for OPA, Sentinel, and Terratest patterns.
Use resources/iso-42001-infra.md for AI governance, continuity, and architecture controls.
Before submitting, run resources/checklist.md.
Vendor-specific execution protocols are injected automatically by oma agent:spawn.
Source files live under ../_shared/runtime/execution-protocols/{vendor}.md.
resources/execution-protocol.mdresources/checklist.mdresources/examples.mdresources/multi-cloud-examples.mdresources/cost-optimization.mdresources/policy-testing-examples.mdresources/iso-42001-infra.mdresources/error-playbook.md../_shared/core/context-loading.md../_shared/core/reasoning-templates.md../_shared/core/clarification-protocol.md../_shared/core/context-budget.md../_shared/core/difficulty-guide.md../_shared/core/lessons-learned.mdterraform, infrastructure-as-code, iac, cloud, aws, gcp, azure, oracle, oci, multi-cloud, devops, provisioning, infrastructure, compute, database, storage, networking, iam, oidc, workload identity, container, kubernetes, serverless, vpc, subnet, load balancer, cdn, secrets management, state management, backend, provider
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 first-fluke/oh-my-agent-oma-tf-infra 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.