Agent skill

GCP Cloud Architect

by alirezarezvani in alirezarezvani/claude-skills

Design GCP architectures for startups and enterprises. An agent skill from alirezarezvani/claude-skills.

MITAuto-check passedDevOps & Cloud

Install GCP Cloud Architect

skills CLI
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a claude-code

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

GitHub CLI
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architect --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/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering-team/skills/gcp-cloud-architect .claude/skills/gcp-cloud-architect && 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-cloud-architect
GitHub stars
28k
Token cost
~3.2k tokens
SKILL.md length
726 words
Files
7 (incl. scripts, references)
Skills in repo
342
Repo updated
First seen
Licence
MIT

At a glance

Design GCP architectures for startups and enterprises. An agent skill from alirezarezvani/claude-skills.

  • Works in 6 steps: Gather Requirements → Design Architecture → Estimate Cost → …
  • Asked to design Google Cloud infrastructure
  • SKILL.md covers Workflow, Tools, Quick Start and Input Requirements, plus 4 more sections
  • Runs Python scripts from its folder; calls gcloud and python

What it does

GCP Cloud Architect is an agent skill from alirezarezvani/claude-skills. Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipelines, optimize GCP costs, or migrate to GCP. Covers Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, and cost optimization.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/architecture_patterns.md`, `references/best_practices.md` and `references/service_selection.md`).

It sits in DevOps & Cloud, covering Cloud architecture, Data warehousing and Serverless. It works with Google Cloud, Google Kubernetes Engine, Cloud Run and Google BigQuery. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.

When your agent uses it

  • Asked to design Google Cloud infrastructure
  • Configure BigQuery pipelines
  • Optimize GCP costs

Example prompts

  • “/gcp-cloud-architect”

Requirements

  • Python 3
  • Docker

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Gather Requirements
  2. Design Architecture
  3. Estimate Cost
  4. Generate IaC
  5. Configure CI/CD
  6. Security Review

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. 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

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • gcloud
    • python

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

  • Network

    Links to these hosts (documentation or services it may open):

    • cloud.google.com

    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.

Context cost

GCP Cloud Architect loads about 3.2k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 726 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~14k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 726 words, ~3,224 tokens.

Download SKILL.mdSave it as .claude/skills/gcp-cloud-architect/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
gcp-cloud-architect
description
Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipelines, optimize GCP costs, or migrate to GCP. Covers Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, and cost optimization.

GCP Cloud Architect

Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.


Workflow

Step 1: Gather Requirements

Collect application specifications:

- Application type (web app, mobile backend, data pipeline, SaaS)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and GCP experience level
- Compliance requirements (GDPR, HIPAA, SOC 2)
- Availability requirements (SLA, RPO/RTO)
Step 2: Design Architecture

Run the architecture designer to get pattern recommendations:

bash
python scripts/architecture_designer.py --input requirements.json

Example output:

json
{
  "recommended_pattern": "serverless_web",
  "service_stack": ["Cloud Storage", "Cloud CDN", "Cloud Run", "Firestore", "Identity Platform"],
  "estimated_monthly_cost_usd": 30,
  "pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling", "No cold starts on Cloud Run min instances"],
  "cons": ["Vendor lock-in", "Regional limitations", "Eventual consistency with Firestore"]
}

Select from recommended patterns:

  • Serverless Web: Cloud Storage + Cloud CDN + Cloud Run + Firestore
  • Microservices on GKE: GKE Autopilot + Cloud SQL + Memorystore + Cloud Pub/Sub
  • Serverless Data Pipeline: Pub/Sub + Dataflow + BigQuery + Looker
  • ML Platform: Vertex AI + Cloud Storage + BigQuery + Cloud Functions

See references/architecture_patterns.md for detailed pattern specifications.

Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

Step 3: Estimate Cost

Analyze estimated costs and optimization opportunities:

bash
python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

Example output:

json
{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size Cloud SQL db-custom-4-16384 to db-custom-2-8192", "savings_usd": 380, "priority": "high" },
    { "action": "Purchase 1-yr committed use discount for GKE nodes", "savings_usd": 290, "priority": "high" },
    { "action": "Move Cloud Storage objects >90 days to Nearline", "savings_usd": 75, "priority": "medium" }
  ],
  "total_potential_savings_usd": 745
}

Output includes:

  • Monthly cost breakdown by service
  • Right-sizing recommendations
  • Committed use discount opportunities
  • Sustained use discount analysis
  • Potential monthly savings

Use the GCP Pricing Calculator for detailed estimates.

Step 4: Generate IaC

Create infrastructure-as-code for the selected pattern:

bash
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1

Example Terraform HCL output (Cloud Run + Firestore):

hcl
terraform {
  required_providers {
    google = {
      source  = "hashicorp/google"
      version = "~> 5.0"
    }
  }
}

provider "google" {
  project = var.project_id
  region  = var.region
}

variable "project_id" {
  description = "GCP project ID"
  type        = string
}

variable "region" {
  description = "GCP region"
  type        = string
  default     = "us-central1"
}

resource "google_cloud_run_v2_service" "api" {
  name     = "${var.environment}-${var.app_name}-api"
  location = var.region

  template {
    containers {
      image = "gcr.io/${var.project_id}/${var.app_name}:latest"
      resources {
        limits = {
          cpu    = "1000m"
          memory = "512Mi"
        }
      }
      env {
        name  = "FIRESTORE_PROJECT"
        value = var.project_id
      }
    }
    scaling {
      min_instance_count = 0
      max_instance_count = 10
    }
  }
}

resource "google_firestore_database" "default" {
  project     = var.project_id
  name        = "(default)"
  location_id = var.region
  type        = "FIRESTORE_NATIVE"
}

Example gcloud CLI deployment:

bash
# Deploy Cloud Run service
gcloud run deploy my-app-api \
  --image gcr.io/$PROJECT_ID/my-app:latest \
  --region us-central1 \
  --platform managed \
  --allow-unauthenticated \
  --memory 512Mi \
  --cpu 1 \
  --min-instances 0 \
  --max-instances 10

# Create Firestore database
gcloud firestore databases create --location=us-central1

Full templates including Cloud CDN, Identity Platform, IAM, and Cloud Monitoring are generated by deployment_manager.py and also available in references/architecture_patterns.md.

Step 5: Configure CI/CD

Set up automated deployment with Cloud Build or GitHub Actions:

yaml
# cloudbuild.yaml
steps:
  - name: 'gcr.io/cloud-builders/docker'
    args: ['build', '-t', 'gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA', '.']

  - name: 'gcr.io/cloud-builders/docker'
    args: ['push', 'gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA']

  - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
    entrypoint: gcloud
    args:
      - 'run'
      - 'deploy'
      - 'my-app-api'
      - '--image=gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA'
      - '--region=us-central1'
      - '--platform=managed'

images:
  - 'gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA'
bash
# Connect repo and create trigger
gcloud builds triggers create github \
  --repo-name=my-app \
  --repo-owner=my-org \
  --branch-pattern="^main$" \
  --build-config=cloudbuild.yaml
Step 6: Security Review

Verify security configuration:

bash
# Review IAM bindings
gcloud projects get-iam-policy $PROJECT_ID --format=json

# Check service account permissions
gcloud iam service-accounts list --project=$PROJECT_ID

# Verify VPC Service Controls (if applicable)
gcloud access-context-manager perimeters list --policy=$POLICY_ID

Security checklist:

  • IAM roles follow least privilege (prefer predefined roles over basic roles)
  • Service accounts use Workload Identity for GKE
  • VPC Service Controls configured for sensitive APIs
  • Cloud KMS encryption keys for customer-managed encryption
  • Cloud Audit Logs enabled for all admin activity
  • Organization policies restrict public access
  • Secret Manager used for all credentials

If deployment fails:

  1. Check the failure reason:
    bash
    gcloud run services describe my-app-api --region us-central1
    gcloud logging read "resource.type=cloud_run_revision" --limit=20
  2. Review Cloud Logging for application errors.
  3. Fix the configuration or container image.
  4. Redeploy:
    bash
    gcloud run deploy my-app-api --image gcr.io/$PROJECT_ID/my-app:latest --region us-central1

Common failure causes:

  • IAM permission errors -- verify service account roles and --allow-unauthenticated flag
  • Quota exceeded -- request quota increase via IAM & Admin > Quotas
  • Container startup failure -- check container logs and health check configuration
  • Region not enabled -- enable the required APIs with gcloud services enable

Tools

architecture_designer.py

Recommends GCP services based on workload requirements.

bash
python scripts/architecture_designer.py --input requirements.json --output design.json

Input: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons

cost_optimizer.py

Analyzes GCP resources for cost savings.

bash
python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000

Output: Recommendations for:

  • Idle resource removal
  • Machine type right-sizing
  • Committed use discounts
  • Storage class transitions
  • Network egress optimization
deployment_manager.py

Generates gcloud CLI deployment scripts and Terraform configurations.

bash
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1

Output: Production-ready deployment scripts with:

  • Cloud Run or GKE deployment
  • Firestore or Cloud SQL setup
  • Identity Platform configuration
  • IAM roles with least privilege
  • Cloud Monitoring and Logging

Quick Start

Web App on Cloud Run (< $100/month)
Ask: "Design a serverless web backend for a mobile app with 1000 users"

Result:
- Cloud Run for API (auto-scaling, no cold start with min instances)
- Firestore for data (pay-per-operation)
- Identity Platform for authentication
- Cloud Storage + Cloud CDN for static assets
- Estimated: $15-40/month
Microservices on GKE ($500-2000/month)
Ask: "Design a scalable architecture for a SaaS platform with 50k users"

Result:
- GKE Autopilot for containerized workloads
- Cloud SQL (PostgreSQL) with read replicas
- Memorystore (Redis) for session caching
- Cloud CDN for global delivery
- Cloud Build for CI/CD
- Multi-zone deployment
Serverless Data Pipeline
Ask: "Design a real-time analytics pipeline for event data"

Result:
- Pub/Sub for event ingestion
- Dataflow (Apache Beam) for stream processing
- BigQuery for analytics and warehousing
- Looker for dashboards
- Cloud Functions for lightweight transforms
Show full SKILL.md (300 more words)Show less
ML Platform
Ask: "Design a machine learning platform for model training and serving"

Result:
- Vertex AI for training and prediction
- Cloud Storage for datasets and model artifacts
- BigQuery for feature store
- Cloud Functions for preprocessing triggers
- Cloud Monitoring for model drift detection

Input Requirements

Provide these details for architecture design:

RequirementDescriptionExample
Application typeWhat you're buildingSaaS platform, mobile backend
Expected scaleUsers, requests/sec10k users, 100 RPS
BudgetMonthly GCP limit$500/month max
Team contextSize, GCP experience3 devs, intermediate
ComplianceRegulatory needsHIPAA, GDPR, SOC 2
AvailabilityUptime requirements99.9% SLA, 1hr RPO

JSON Format:

json
{
  "application_type": "saas_platform",
  "expected_users": 10000,
  "requests_per_second": 100,
  "budget_monthly_usd": 500,
  "team_size": 3,
  "gcp_experience": "intermediate",
  "compliance": ["SOC2"],
  "availability_sla": "99.9%"
}

Output Formats

Architecture Design
  • Pattern recommendation with rationale
  • Service stack diagram (ASCII)
  • Monthly cost estimate and trade-offs
IaC Templates
  • Terraform HCL: Production-ready Google provider configs
  • gcloud CLI: Scripted deployment commands
  • Cloud Build YAML: CI/CD pipeline definitions
Cost Analysis
  • Current spend breakdown with optimization recommendations
  • Priority action list (high/medium/low) and implementation checklist

Anti-Patterns

Anti-PatternWhy It FailsBetter Approach
Using default VPC for productionNo isolation, shared firewall rulesCreate custom VPC with private subnets
Over-provisioning GKE node poolsWasted cost on idle capacityUse GKE Autopilot or cluster autoscaler
Storing secrets in environment variablesVisible in Cloud Console, logsUse Secret Manager with Workload Identity
Ignoring sustained use discountsMissing 20-30% automatic savingsRight-size VMs for consistent baseline usage
Single-region deployment for SaaSOne region outage = full downtimeMulti-region with Cloud Load Balancing
BigQuery on-demand for heavy workloadsUnpredictable costs at scaleUse BigQuery slots (flat-rate) for consistent workloads
Running Cloud Functions for long tasks9-minute timeout, cold startsUse Cloud Run for tasks > 60 seconds

Cross-References

SkillRelationship
engineering-team/aws-solution-architectAWS equivalent — same 6-step workflow, different services
engineering-team/azure-cloud-architectAzure equivalent — completes the cloud trifecta
engineering-team/senior-devopsBroader DevOps scope — pipelines, monitoring, containerization
engineering/terraform-patternsIaC implementation — use for Terraform modules targeting GCP
engineering/ci-cd-pipeline-builderPipeline construction — automates Cloud Build and deployment

Reference Documentation

DocumentContents
references/architecture_patterns.md6 patterns: serverless, GKE microservices, three-tier, data pipeline, ML platform, multi-region
references/service_selection.mdDecision matrices for compute, database, storage, messaging
references/best_practices.mdNaming, labels, IAM, networking, monitoring, disaster recovery

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

Files

SKILL.md and 6 other files (scripts, references) in engineering-team/skills/gcp-cloud-architect of alirezarezvani/claude-skills.

  • SKILL.md
  • references/architecture_patterns.md
  • references/best_practices.md
  • references/service_selection.md
  • scripts/architecture_designer.py
  • scripts/cost_optimizer.py
  • scripts/deployment_manager.py

Open the folder on GitHubat commit 19392f7

Compare with similar skills

GCP Cloud Architect 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.

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Gke Cost Analysisgoogle/skills21k—~1.5kAutomated safety check: PassApache-2.0
Cloud Monitoring Metric Selectiongoogle/skills21k—~2.4kAutomated safety check: PassApache-2.0
GCP DrawIO Diagram Generatora5c-ai/babysitter1.8k—~3.7kAutomated safety check: PassMIT
Dd GCP Integrationdatadog-labs/agent-skills177—~8kAutomated safety check: NotesMIT

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Questions about GCP Cloud Architect

What does GCP Cloud Architect do?

Design GCP architectures for startups and enterprises. An agent skill from alirezarezvani/claude-skills. GCP Cloud Architect is an agent skill from alirezarezvani/claude-skills. Design GCP architectures for startups and enterprises.

When should I use GCP Cloud Architect?

GCP Cloud Architect fits situations like: asked to design Google Cloud infrastructure; configure BigQuery pipelines; optimize GCP costs.

How do I install GCP Cloud Architect in Claude Code?

Run `npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a claude-code`. Or copy the skill folder (engineering-team/skills/gcp-cloud-architect in alirezarezvani/claude-skills) into .claude/skills/gcp-cloud-architect in your project. Claude Code loads it when a task matches its description.

How do I install GCP Cloud Architect in Codex?

Run `npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a codex`. Or copy the skill folder (engineering-team/skills/gcp-cloud-architect in alirezarezvani/claude-skills) into .agents/skills/gcp-cloud-architect in your project. Codex loads it when a task matches its description.

Can I use GCP Cloud Architect 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 alirezarezvani/claude-skills --skill gcp-cloud-architect -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-cloud-architect, .gemini/skills/gcp-cloud-architect, .github/skills/gcp-cloud-architect and .opencode/skills/gcp-cloud-architect in your project.

What does GCP Cloud Architect need to run?

Going by SKILL.md and its folder, GCP Cloud Architect needs Python for the scripts in its folder and the command-line tools its instructions call (gcloud and python). Our summary lists: Python 3; Docker.

Does GCP Cloud Architect access the network?

SKILL.md names 1 domain. As links in the text: cloud.google.com. This is read from the text; nothing was executed.

Is GCP Cloud Architect 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does GCP Cloud Architect use?

GCP Cloud Architect is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does GCP Cloud Architect use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to GCP Cloud Architect?

Skills that share tags, products or a category with GCP Cloud Architect: Deploying On GCP (ancoleman/ai-design-components, 526 stars), Gke Cost Analysis (google/skills, 21k stars), Cloud Monitoring Metric Selection (google/skills, 21k stars) and GCP DrawIO Diagram Generator (a5c-ai/babysitter, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains GCP Cloud Architect?

alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.

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