Agent skill

GCP Compute

by sickn33 in sickn33/agentic-awesome-skills

Manage Compute Engine instances and instance templates. An agent skill from sickn33/agentic-awesome-skills.

MITAuto-check passedDevOps & Cloud

Install GCP Compute

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill gcp-compute -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills gcp-compute --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gcp-compute .claude/skills/gcp-compute && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gcp-compute
GitHub stars
47k
Used in
2 other repos
Token cost
~2.6k tokens
SKILL.md length
343 words
Files
1
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Manage Compute Engine instances and instance templates. An agent skill from sickn33/agentic-awesome-skills.

  • Deploying compute resources on GCP
  • SKILL.md covers When to Use, Prerequisites, Machine Types Reference and Create an Instance, plus 9 more sections
  • Calls gcloud, apt-get and curl

What it does

GCP Compute is an agent skill from sickn33/agentic-awesome-skills. Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

It sits in DevOps & Cloud. It works with Google Cloud. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Deploying compute resources on GCP

Example prompts

  • “/gcp-compute”

Requirements

  • Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gcloud
    • apt-get
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gcloud and curl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

    From compatibility in the SKILL.md frontmatter.

Context cost

GCP Compute loads about 2.6k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 343 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 343 words, ~2,577 tokens.

Download SKILL.mdSave it as .claude/skills/gcp-compute/SKILL.md (or your agent's skills folder).
name
gcp-compute
description
Manage Compute Engine instances and instance templates. Configure managed instance groups and preemptible VMs. Use when deploying compute resources on GCP.
compatibility
Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.
category
devops
risk
critical
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
1.0

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
  • 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

Limitations

  • Infrastructure commands can disrupt services: confirm target host/scope and have backups/snapshots before mutating state.
  • Docs-only import: upstream scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/gcp-compute of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 680176d

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

GCP Compute next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

GCP Compute compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
GCP Compute this skillsickn33/agentic-awesome-skills47k2 repos~2.6kAutomated safety check: PassMIT
Cloud Cost Optimizationwshobson/agents40k14 repos~1.7kAutomated safety check: PassMIT
Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit2606 repos~1.1kAutomated safety check: NotesCustom licence
Terravision Cloud Diagramspatrickchugh/terravision1.6k—~5.6kAutomated safety check: NotesAGPL-3.0-only
Thesvgglincker/thesvg2.8k—~1.5kAutomated safety check: PassMIT
TerrasharkLukasNiessen/terrashark715—~843Automated safety check: PassMIT

Similar skills

  • Cuts cloud spend across AWS, Azure, GCP and OCI with cost tagging, rightsizing, commitment and spot pricing models, and architecture changes.

    40k GitHub starsUsed in 14 repos~1.7k tokens
    DevOps & CloudAuto-check passed
  • Senior DevOps Toolkit

    maslennikov-ig/claude-code-orchestrator-kit

    Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup…

    260 GitHub starsUsed in 6 repos~1.1k tokens
    DevOps & CloudAuto-check: notes
  • Terravision Cloud Diagrams

    patrickchugh/terravision

    Draw cloud architecture diagrams for AWS, Azure or GCP with the official provider icon sets, using TerraVision.

    1.6k GitHub stars~5.6k tokensUpdated 2 days ago
    DevOps & CloudAuto-check: notes
  • Thesvg

    glincker/thesvg

    Fetch brand SVG logos and cloud architecture icons (AWS, Azure, GCP) from theSVG.

    2.8k GitHub stars~1.5k tokensUpdated today
    DevOps & CloudAuto-check passed
  • Terrashark

    LukasNiessen/terrashark

    Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps.

    715 GitHub stars~843 tokensUpdated 6 days ago
    DevOps & CloudAuto-check passed
  • Devops

    nicepkg/auto-company

    Deploy to Cloudflare (Workers, R2, D1), Docker, GCP (Cloud Run, GKE), Kubernetes (kubectl, Helm).

    194 GitHub starsUsed in 2 repos~814 tokens
    DevOps & CloudAuto-check passed

More from sickn33/agentic-awesome-skills

All 1,493 skills in this repo
  • Liuguang Banlan UI

    sickn33/agentic-awesome-skills

    Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • User Thoughts Memory

    sickn33/agentic-awesome-skills

    Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • Using LWC Memory and Graphs

    sickn33/agentic-awesome-skills

    Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.

    47k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed
  • Find Complementary Founders

    sickn33/agentic-awesome-skills

    Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.

    47k GitHub starsUsed in 1 repo~4.8k tokens
    Auto-check passed
  • Whatsapp Cloud API

    sickn33/agentic-awesome-skills

    Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.

    47k GitHub starsUsed in 2 repos~4.5k tokens
    Auto-check passed
  • Cline Pilot

    sickn33/agentic-awesome-skills

    Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.

    47k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed

Works with

Categories

Questions about GCP Compute

What does GCP Compute do?

Manage Compute Engine instances and instance templates. An agent skill from sickn33/agentic-awesome-skills. GCP Compute is an agent skill from sickn33/agentic-awesome-skills. Manage Compute Engine instances and instance templates.

When should I use GCP Compute?

GCP Compute fits situations like: deploying compute resources on GCP.

How do I install GCP Compute in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill gcp-compute -a claude-code`. Or copy the skill folder (skills/gcp-compute in sickn33/agentic-awesome-skills) into .claude/skills/gcp-compute in your project. Claude Code loads it when a task matches its description.

How do I install GCP Compute in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill gcp-compute -a codex`. Or copy the skill folder (skills/gcp-compute in sickn33/agentic-awesome-skills) into .agents/skills/gcp-compute in your project. Codex loads it when a task matches its description.

Can I use GCP Compute in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill gcp-compute -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gcp-compute, .gemini/skills/gcp-compute, .github/skills/gcp-compute and .opencode/skills/gcp-compute in your project.

What does GCP Compute need to run?

Going by SKILL.md and its folder, GCP Compute needs the command-line tools its instructions call (gcloud, apt-get and curl). Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled..

Does GCP Compute access the network?

SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is GCP Compute safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does GCP Compute use?

GCP Compute is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does GCP Compute use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to GCP Compute?

Skills that share tags, products or a category with GCP Compute: Cloud Cost Optimization (wshobson/agents, 40k stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), Terravision Cloud Diagrams (patrickchugh/terravision, 1.6k stars) and Thesvg (glincker/thesvg, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains GCP Compute?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.