gcp-compute
gcp-compute is an engineering AI skill with a core value of Manage Compute Engine instances and instance templates. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.
Quick Facts
mkdir -p ./skills/gcp-compute && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/gcp-compute/SKILL.md -o ./skills/gcp-compute/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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
gcloud auth list
gcloud config set project $PROJECT_ID
gcloud services enable compute.googleapis.comMachine Types Reference
| Family | Example | vCPUs | Memory | Use Case |
|--------|---------|-------|--------|----------|
| E2 | e2-micro | 0.25 | 1 GB | Dev/test, microservices |
| E2 | e2-medium | 1 | 4 GB | Light web servers |
| N2 | n2-standard-4 | 4 | 16 GB | General-purpose production |
| N2 | n2-highmem-8 | 8 | 64 GB | In-memory caches, databases |
| C2 | c2-standard-16 | 16 | 64 GB | Compute-intensive, HPC |
# List machine types available in a zone
gcloud compute machine-types list --zones=us-central1-a --filter="name~'e2-'"
# Create a custom machine type (6 vCPUs, 24 GB RAM)
gcloud compute instances create custom-vm \
--custom-cpu=6 --custom-memory=24GB \
--zone=us-central1-a \
--image-family=debian-12 --image-project=debian-cloudCreate an Instance
# Production instance with shielded VM and startup script
gcloud compute instances create web-server \
--machine-type=e2-medium \
--zone=us-central1-a \
--image-family=debian-12 \
--image-project=debian-cloud \
--boot-disk-size=20GB \
--boot-disk-type=pd-balanced \
--tags=http-server,https-server \
--labels=env=production,team=backend \
--metadata=enable-oslogin=TRUE \
--shielded-secure-boot \
--shielded-vtpm \
--shielded-integrity-monitoring
# Instance with a startup script and service account
gcloud compute instances create app-server \
--machine-type=e2-standard-2 \
--zone=us-central1-a \
--image-family=ubuntu-2204-lts \
--image-project=ubuntu-os-cloud \
--boot-disk-size=50GB \
--metadata-from-file=startup-script=startup.sh \
--service-account=app-sa@${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=noStartup Script Example
#!/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
# 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 manag🎯 Best For
- Claude users
- AI users
💡 Use Cases
- Using gcp-compute in daily workflow
- Automating repetitive engineering tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply gcp-compute to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install gcp-compute?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/gcp-compute/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.