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Gcp Compute

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BagelHole
gcp-compute

Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namegcp-compute
Stars
1.1K
Forks
161
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Gcp Compute 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/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-gcp/gcp-compute
  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/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/infrastructure/cloud-gcp/gcp-compute .claude/skills/gcp-compute
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gcp Compute 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 Gcp Compute 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 Gcp Compute 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.

GCP Compute Engine

Deploy, manage, and scale Compute Engine virtual machines on Google Cloud Platform.

When to Use

  • Deploying web servers, application backends, or batch-processing workloads on GCP
  • Running workloads that need full OS-level control (unlike Cloud Run or App Engine)
  • Creating managed instance groups for auto-healing and auto-scaling behind a load balancer
  • Provisioning GPU-attached VMs for ML training or rendering pipelines
  • Cost-optimizing non-critical workloads with preemptible or spot VMs

Prerequisites

  • Google Cloud SDK (gcloud) installed and authenticated
  • A GCP project with the Compute Engine API enabled
  • IAM role roles/compute.admin or scoped roles for instance management
bash
gcloud auth list
gcloud config set project $PROJECT_ID
gcloud services enable compute.googleapis.com

Machine Types Reference

FamilyExamplevCPUsMemoryUse Case
E2e2-micro0.251 GBDev/test, microservices
E2e2-medium14 GBLight web servers
N2n2-standard-4416 GBGeneral-purpose production
N2n2-highmem-8864 GBIn-memory caches, databases
C2c2-standard-161664 GBCompute-intensive, HPC
bash
# List machine types available in a zone
gcloud compute machine-types list --zones=us-central1-a --filter="name~'e2-'"

# Create a custom machine type (6 vCPUs, 24 GB RAM)
gcloud compute instances create custom-vm \
  --custom-cpu=6 --custom-memory=24GB \
  --zone=us-central1-a \
  --image-family=debian-12 --image-project=debian-cloud

Create an Instance

bash
# Production instance with shielded VM and startup script
gcloud compute instances create web-server \
  --machine-type=e2-medium \
  --zone=us-central1-a \
  --image-family=debian-12 \
  --image-project=debian-cloud \
  --boot-disk-size=20GB \
  --boot-disk-type=pd-balanced \
  --tags=http-server,https-server \
  --labels=env=production,team=backend \
  --metadata=enable-oslogin=TRUE \
  --shielded-secure-boot \
  --shielded-vtpm \
  --shielded-integrity-monitoring

# Instance with a startup script and service account
gcloud compute instances create app-server \
  --machine-type=e2-standard-2 \
  --zone=us-central1-a \
  --image-family=ubuntu-2204-lts \
  --image-project=ubuntu-os-cloud \
  --boot-disk-size=50GB \
  --metadata-from-file=startup-script=startup.sh \
  --service-account=app-sa@${PROJECT_ID}.iam.gserviceaccount.com \
  --scopes=cloud-platform

# Instance with an additional data disk
gcloud compute instances create db-server \
  --machine-type=n2-highmem-4 \
  --zone=us-central1-a \
  --image-family=debian-12 --image-project=debian-cloud \
  --boot-disk-size=20GB \
  --create-disk=name=data-disk,size=200GB,type=pd-ssd,auto-delete=no

Startup Script Example

bash
#!/bin/bash
# startup.sh - runs on first boot and every reboot
set -euo pipefail
apt-get update && apt-get install -y nginx
systemctl enable nginx && systemctl start nginx
curl -X PUT -H "Metadata-Flavor: Google" \
  "http://metadata.google.internal/computeMetadata/v1/instance/guest-attributes/startup/status" \
  -d "complete"

Instance Templates and Managed Instance Groups

bash
# Create an instance template
gcloud compute instance-templates create web-template \
  --machine-type=e2-medium \
  --image-family=debian-12 --image-project=debian-cloud \
  --boot-disk-size=20GB --tags=http-server \
  --metadata-from-file=startup-script=startup.sh

# Create a regional managed instance group (MIG) with health check
gcloud compute health-checks create http http-health-check \
  --port=80 --request-path=/healthz \
  --check-interval=10s --timeout=5s \
  --healthy-threshold=2 --unhealthy-threshold=3

gcloud compute instance-groups managed create web-mig \
  --template=web-template --size=3 \
  --region=us-central1 \
  --health-check=http-health-check --initial-delay=120

# Configure autoscaling
gcloud compute instance-groups managed set-autoscaling web-mig \
  --region=us-central1 \
  --min-num-replicas=2 --max-num-replicas=10 \
  --target-cpu-utilization=0.65 --cool-down-period=90

# Rolling update to a new template
gcloud compute instance-groups managed rolling-action start-update web-mig \
  --version=template=web-template-v2 \
  --region=us-central1 --max-surge=3 --max-unavailable=0

Preemptible and Spot VMs

bash
# Spot VM (recommended over legacy preemptible)
gcloud compute instances create spot-worker \
  --machine-type=n2-standard-8 \
  --zone=us-central1-a \
  --image-family=debian-12 --image-project=debian-cloud \
  --provisioning-model=SPOT \
  --instance-termination-action=STOP

# Spot instance template for batch MIG
gcloud compute instance-templates create batch-template \
  --machine-type=n2-standard-4 \
  --image-family=debian-12 --image-project=debian-cloud \
  --provisioning-model=SPOT \
  --instance-termination-action=DELETE

Snapshots and Images

bash
# Create a snapshot
gcloud compute disks snapshot web-server \
  --zone=us-central1-a \
  --snapshot-names=web-server-snap-$(date +%Y%m%d)

# Scheduled snapshot policy
gcloud compute resource-policies create snapshot-schedule daily-backup \
  --region=us-central1 --max-retention-days=14 \
  --daily-schedule --start-time=03:00

gcloud compute disks add-resource-policies web-server \
  --zone=us-central1-a --resource-policies=daily-backup

# Create a custom image from an instance
gcloud compute instances stop web-server --zone=us-central1-a
gcloud compute images create web-golden-image \
  --source-disk=web-server --source-disk-zone=us-central1-a \
  --family=web-server --labels=version=v1

Terraform Configuration

hcl
resource "google_compute_instance" "web" {
  name         = "web-server"
  machine_type = "e2-medium"
  zone         = "us-central1-a"
  tags         = ["http-server", "https-server"]

  boot_disk {
    initialize_params {
      image = "debian-cloud/debian-12"
      size  = 20
      type  = "pd-balanced"
    }
  }

  network_interface {
    subnetwork = google_compute_subnetwork.main.id
    access_config {}
  }

  metadata_startup_script = file("${path.module}/startup.sh")

  service_account {
    email  = google_service_account.app.email
    scopes = ["cloud-platform"]
  }

  shielded_instance_config {
    enable_secure_boot          = true
    enable_vtpm                 = true
    enable_integrity_monitoring = true
  }
}

resource "google_compute_instance_template" "web" {
  name_prefix  = "web-"
  machine_type = "e2-medium"

  disk {
    source_image = "debian-cloud/debian-12"
    auto_delete  = true
    boot         = true
    disk_size_gb = 20
  }

  network_interface {
    subnetwork = google_compute_subnetwork.main.id
  }

  lifecycle { create_before_destroy = true }
}

resource "google_compute_region_instance_group_manager" "web" {
  name               = "web-mig"
  base_instance_name = "web"
  region             = "us-central1"

  version {
    instance_template = google_compute_instance_template.web.id
  }

  target_size = 3
  named_port { name = "http"; port = 80 }

  auto_healing_policies {
    health_check      = google_compute_health_check.http.id
    initial_delay_sec = 120
  }
}

resource "google_compute_region_autoscaler" "web" {
  name   = "web-autoscaler"
  region = "us-central1"
  target = google_compute_region_instance_group_manager.web.id

  autoscaling_policy {
    min_replicas    = 2
    max_replicas    = 10
    cooldown_period = 90
    cpu_utilization { target = 0.65 }
  }
}

Common Operations

bash
# SSH into an instance
gcloud compute ssh web-server --zone=us-central1-a

# List all instances with status
gcloud compute instances list \
  --format="table(name,zone,status,machineType.basename())"

# Stop / start / resize
gcloud compute instances stop web-server --zone=us-central1-a
gcloud compute instances set-machine-type web-server \
  --machine-type=e2-standard-4 --zone=us-central1-a
gcloud compute instances start web-server --zone=us-central1-a

# View serial port output (debug startup scripts)
gcloud compute instances get-serial-port-output web-server --zone=us-central1-a

Troubleshooting

SymptomCauseFix
Instance stuck in STAGINGQuota exceeded or resource unavailableCheck quota with gcloud compute project-info describe; try another zone
Startup script not runningSyntax errors or wrong metadata keyCheck serial output; ensure key is startup-script not startup_script
Cannot SSHFirewall blocks port 22 or OS Login misconfiguredAdd firewall rule for tcp:22; verify enable-oslogin metadata
Preempted too oftenZone resource pressureUse Spot VM with STOP action; spread across zones in a MIG
Disk out of spaceBoot disk too smallUse gcloud compute disks resize; enable --storage-auto-increase for data disks
MIG not healingHealth check misconfigured or initial delay too shortVerify health check path returns 200; increase --initial-delay

Related Skills

  • gcp-networking - VPC, firewall rules, and load balancers for Compute Engine
  • terraform-gcp - Provision Compute Engine resources with Infrastructure as Code
  • gcp-gke - When workloads are better suited for containers than VMs
  • gcp-cloud-sql - Managed databases that Compute Engine applications connect to

Frequently asked questions

What does the Gcp Compute AI skill do?

Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.

Why use Gcp Compute on TypingMind?

Because you install it once and use it with any model. Gcp Compute 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 Gcp Compute in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-gcp/gcp-compute. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gcp Compute?

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 Gcp Compute?

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

Is the Gcp Compute AI skill free?

Yes. It is published on GitHub by BagelHole 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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