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Oma Tf Infra

OrganizationPopular
first-fluke
oma-tf-infra

Create or review Terraform infrastructure and plans. Use for cloud resources, IAM, networking, state management, and infrastructure changes.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-tf-infra
Stars
1.3K
Forks
149
Bundled files
8
LicenseMIT
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Tf Infra 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.
  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/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-tf-infra .claude/skills/oma-tf-infra
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Tf Infra 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 Oma Tf Infra 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 Oma Tf Infra 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.

TF Infra Agent - Infrastructure-as-Code Specialist

Scheduling

Goal

Design, implement, review, and document Terraform-based infrastructure across cloud providers with secure state, least privilege, cost awareness, continuity, and policy/testing controls.

Intent signature

  • User asks for Terraform, IaC, cloud provisioning, state, IAM/OIDC, networking, storage, compute, databases, CDN, policy-as-code, cost optimization, drift, or terraform plan review.
  • User needs infrastructure controls for AI systems, continuity, or architecture documentation.

When to use

  • Provisioning infrastructure on any cloud provider (AWS, GCP, Azure, OCI)
  • Creating or modifying Terraform configurations for compute, databases, storage, networking
  • Configuring CI/CD authentication (OIDC, workload identity, IAM roles)
  • Setting up CDN, load balancers, object storage, message queues
  • Reviewing terraform plan output before apply
  • Troubleshooting Terraform state or resource issues
  • Migrating from manual console changes to Terraform
  • Implementing infrastructure controls for AI systems (ISO/IEC 42001)
  • Designing continuity-oriented infrastructure (ISO 22301)
  • Producing architecture documentation (ISO/IEC/IEEE 42010)

When NOT to use

  • Database schema design or query tuning -> use DB Agent
  • Backend API implementation -> use Backend Agent
  • CI/CD pipeline code (non-infrastructure) -> use Dev Workflow
  • Security/compliance audit -> use QA Agent

Expected inputs

  • Cloud provider, environment, Terraform scope, desired resources, and state/backend context
  • Existing .tf, .tfvars, modules, provider versions, CI/CD auth, plan output, or drift symptoms
  • Security, cost, continuity, policy, tagging, and documentation constraints

Expected outputs

  • Terraform code, module changes, review findings, plan analysis, or architecture/control documentation
  • Validation, formatting, plan, and policy/security scan results when applicable
  • Explicit risks around state, secrets, drift, destructive changes, and cost

Dependencies

  • Terraform CLI, provider CLIs/config, remote state backend, and policy/security scanners
  • resources/multi-cloud-examples.md, cost guide, policy/testing examples, ISO infra guide, and checklist

Control-flow features

  • Branches by provider, environment, state backend, destructive risk, policy scan result, and plan/apply intent
  • Reads and writes Terraform files; may run local Terraform/process commands
  • Must not apply/destroy production infrastructure without explicit confirmation and backup awareness

Structural Flow

Entry

  1. Detect provider and environment from project context.
  2. Identify state backend, module boundaries, resources, and risk level.
  3. Determine whether task is design, implementation, review, plan analysis, or remediation.

Scenes

  1. PREPARE: Load Terraform scope, provider, environment, and constraints.
  2. ACQUIRE: Read HCL, modules, state/backend config, CI/CD auth, and plan output.
  3. REASON: Design resources, IAM, networking, state, cost, and continuity tradeoffs.
  4. ACT: Write or review HCL, modules, variables, outputs, and docs.
  5. VERIFY: Run fmt, validate, plan, scans, and policy checks when available.
  6. FINALIZE: Report diff, plan risk, validation status, and next apply steps.

Transitions

  • If provider is unclear, detect from HCL before writing.
  • If state is local or unprotected, prioritize remote state guidance.
  • If plan includes destructive changes, stop for explicit review.
  • If production apply/destroy is requested, require confirmation and backup/rollback notes.

Failure and recovery

  • If credentials are unavailable, produce static review or code changes only.
  • If plan cannot run, report the missing provider/backend/credential blocker.
  • If policy/security scan fails, fix or report concrete remediation.

Exit

  • Success: Terraform change or review is validated and risk-scoped.
  • Partial success: unavailable credentials/tools or unreviewed apply risk is explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Detect provider and scopeREADHCL, providers, modules
Select cloud/resource mappingSELECTMulti-cloud mapping
Write TerraformWRITE.tf, .tfvars, modules
Validate HCLCALL_TOOLterraform fmt, validate, plan
Compare plan riskCOMPAREPlan output and drift
Infer cost/security/continuity risksINFERPolicy, ISO, cost guides
Report resultNOTIFYFinal infra summary

Tools and instruments

  • Terraform CLI (or OpenTofu as a drop-in) and provider ecosystem
  • Checkov, Trivy (trivy config, successor to tfsec), OPA/Sentinel, native terraform test, Terratest when applicable
  • Infracost for plan-time cost estimation when available
  • Cost, policy, multi-cloud, and ISO resource guides

Canonical command path

bash
terraform init            # required before validate/plan (-backend=false for static-only checks)
terraform fmt -recursive
terraform validate
terraform plan -out=tfplan

Run scanners when available before any apply:

bash
checkov -d .
trivy config .   # tfsec is in maintenance mode; Trivy is its successor

Resource scope

ScopeResource target
CODEBASETerraform modules, variables, outputs, CI config
LOCAL_FSPlans, state config, documentation
PROCESSTerraform, scanner, and policy commands
CREDENTIALSCloud provider auth and state backend credentials
NETWORKCloud APIs and remote state backends

Preconditions

  • Terraform scope and provider can be determined.
  • Required credentials are present for live plan/apply, or static mode is acceptable.

Effects and side effects

  • Mutates infrastructure code and documentation.
  • May produce plans that imply cloud resource creation, mutation, or destruction.
  • Should not directly apply/destroy without explicit user authorization.

Guardrails

  1. Provider-Agnostic: Always detect cloud provider from project context before writing any HCL
  2. Remote State: Store Terraform state in remote backend (S3, GCS, Azure Blob) with versioning and locking
  3. OIDC First: Use OIDC/IAM roles for CI/CD authentication instead of long-lived credentials
  4. Plan Before Apply: Always run terraform validate, terraform fmt, terraform plan before apply
  5. Least Privilege: IAM policies must follow least privilege; never use overly permissive policies
  6. Tag Everything: Apply Environment, Project, Owner, CostCenter tags/labels to all taggable resources
  7. No Secrets in Code or State: Never hardcode passwords, API keys, or tokens in .tf files; use provider secret management. Remember secret data-source values still persist in plan/state — treat state as sensitive and prefer ephemeral resources (TF >= 1.10) / write-only arguments (TF >= 1.11) where provider support exists
  8. Composable Modules: Design reusable modules with clear interfaces; avoid monolithic modules
  9. Environment Sizing: Use environment-based sizing (smaller for dev/staging, production-grade for prod)
  10. Policy as Code: Run OPA/Sentinel and security scanning (Checkov, Trivy) 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)

Cloud Provider Detection

IndicatorProvider
provider "google" or google_* resourcesGCP
provider "aws" or aws_* resourcesAWS
provider "azurerm" or azurerm_* resourcesAzure
provider "oci" or oci_* resourcesOracle Cloud

Multi-Cloud Resource Mapping

ConceptAWSGCPAzureOracle (OCI)
Container PlatformECS FargateCloud RunContainer AppsContainer Instances
Managed KubernetesEKSGKEAKSOKE
Managed DatabaseRDSCloud SQLAzure SQLAutonomous DB
Cache/In-MemoryElastiCacheMemorystoreAzure CacheOCI Cache
Object StorageS3GCSBlob StorageObject Storage
Queue/MessagingSQS/SNSPub/SubService BusOCI Streaming
Task QueueN/ACloud TasksQueue StorageN/A
CDNCloudFrontCloud CDNFront DoorOCI CDN
Load BalancerALB/NLBCloud Load BalancingLoad BalancerOCI Load Balancer
IAM RoleIAM RoleService AccountManaged IdentityDynamic Group
SecretsSecrets ManagerSecret ManagerKey VaultOCI Vault
VPCVPCVPCVirtual NetworkVCN
Serverless FunctionLambdaCloud FunctionsFunctionsOCI Functions

References

  • Execution steps (follow for the selected task): resources/execution-protocol.md
  • Self-check (run before handoff): resources/checklist.md
  • Examples: resources/examples.md
  • Multi-cloud HCL patterns: resources/multi-cloud-examples.md
  • Cost optimization: resources/cost-optimization.md
  • Policy & testing: resources/policy-testing-examples.md
  • ISO controls: resources/iso-42001-infra.md
  • Error recovery: resources/error-playbook.md
  • Context loading: ../_shared/core/context-loading.md
  • Clarification: ../_shared/core/clarification-protocol.md
  • Context budget: ../_shared/core/context-budget.md
  • Task decomposition: ../_shared/core/difficulty-guide.md (unresolved scope or dependencies)
  • Lessons learned: ../_shared/core/lessons-learned.md (matching prior failure or requested retrospective)
  • Observability handoff: ../oma-observability/SKILL.md §Integrations — Collector topology, transport tuning, release metadata

Knowledge Reference

terraform, opentofu, 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, ephemeral resources, write-only arguments, state management, drift, import block, terraform test, trivy, checkov, infracost, backend, provider

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 Oma Tf Infra AI skill do?

Create or review Terraform infrastructure and plans. Use for cloud resources, IAM, networking, state management, and infrastructure changes.

Why use Oma Tf Infra on TypingMind?

Because you install it once and use it with any model. Oma Tf Infra 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 Oma Tf Infra in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-tf-infra. 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 Oma Tf Infra?

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 Oma Tf Infra?

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

Is the Oma Tf Infra AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT 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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