Deploying On GCP
ancoleman/ai-design-components
Implement applications using Google Cloud Platform (GCP) services.
Design GCP architectures for startups and enterprises. An agent skill from alirezarezvani/claude-skills.
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architect --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "gcp-cloud-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architect into .claude/skills/gcp-cloud-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gcp-cloud-architect", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architectType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architect --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering-team/skills/gcp-cloud-architect .agents/skills/gcp-cloud-architect && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gcp-cloud-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architect into .agents/skills/gcp-cloud-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gcp-cloud-architect", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architect --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering-team/skills/gcp-cloud-architect .cursor/skills/gcp-cloud-architect && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gcp-cloud-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architect into .cursor/skills/gcp-cloud-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gcp-cloud-architect", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-skills.git --path engineering-team/skills/gcp-cloud-architect--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architect --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering-team/skills/gcp-cloud-architect .gemini/skills/gcp-cloud-architect && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gcp-cloud-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architect into .gemini/skills/gcp-cloud-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gcp-cloud-architect", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architectInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering-team/skills/gcp-cloud-architect .github/skills/gcp-cloud-architect && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gcp-cloud-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architect into .github/skills/gcp-cloud-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gcp-cloud-architect", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills gcp-cloud-architect --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering-team/skills/gcp-cloud-architect .opencode/skills/gcp-cloud-architect && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gcp-cloud-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/skills/gcp-cloud-architect into .opencode/skills/gcp-cloud-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gcp-cloud-architect", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
gcp-cloud-architectDesign 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. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.
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.
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
gcloudpythonFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
cloud.google.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 726 words, ~3,224 tokens.
.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.Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.
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)Run the architecture designer to get pattern recommendations:
python scripts/architecture_designer.py --input requirements.jsonExample output:
{
"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:
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.
Analyze estimated costs and optimization opportunities:
python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000Example output:
{
"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:
Use the GCP Pricing Calculator for detailed estimates.
Create infrastructure-as-code for the selected pattern:
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1Example Terraform HCL output (Cloud Run + Firestore):
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:
# Deploy Cloud Run service
gcloud run deploy my-app-api \
--image gcr.io/$PROJECT_ID/my-app:latest \
--region us-central1 \
--platform managed \
--allow-unauthenticated \
--memory 512Mi \
--cpu 1 \
--min-instances 0 \
--max-instances 10
# Create Firestore database
gcloud firestore databases create --location=us-central1Full templates including Cloud CDN, Identity Platform, IAM, and Cloud Monitoring are generated by
deployment_manager.pyand also available inreferences/architecture_patterns.md.
Set up automated deployment with Cloud Build or GitHub Actions:
# cloudbuild.yaml
steps:
- name: 'gcr.io/cloud-builders/docker'
args: ['build', '-t', 'gcr.io/$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:$COMMIT_SHA'# Connect repo and create trigger
gcloud builds triggers create github \
--repo-name=my-app \
--repo-owner=my-org \
--branch-pattern="^main$" \
--build-config=cloudbuild.yamlVerify security configuration:
# 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=$POLICY_IDSecurity checklist:
If deployment fails:
gcloud run services describe my-app-api --region us-central1
gcloud logging read "resource.type=cloud_run_revision" --limit=20gcloud run deploy my-app-api --image gcr.io/$PROJECT_ID/my-app:latest --region us-central1Common failure causes:
--allow-unauthenticated flaggcloud services enableRecommends GCP services based on workload requirements.
python scripts/architecture_designer.py --input requirements.json --output design.jsonInput: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons
Analyzes GCP resources for cost savings.
python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000Output: Recommendations for:
Generates gcloud CLI deployment scripts and Terraform configurations.
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1Output: Production-ready deployment scripts with:
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/monthAsk: "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 deploymentAsk: "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 transformsAsk: "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 detectionProvide these details for architecture design:
| Requirement | Description | Example |
|---|---|---|
| Application type | What you're building | SaaS platform, mobile backend |
| Expected scale | Users, requests/sec | 10k users, 100 RPS |
| Budget | Monthly GCP limit | $500/month max |
| Team context | Size, GCP experience | 3 devs, intermediate |
| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |
| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |
JSON Format:
{
"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%"
}| Anti-Pattern | Why It Fails | Better Approach |
|---|---|---|
| Using default VPC for production | No isolation, shared firewall rules | Create custom VPC with private subnets |
| Over-provisioning GKE node pools | Wasted cost on idle capacity | Use GKE Autopilot or cluster autoscaler |
| Storing secrets in environment variables | Visible in Cloud Console, logs | Use Secret Manager with Workload Identity |
| Ignoring sustained use discounts | Missing 20-30% automatic savings | Right-size VMs for consistent baseline usage |
| Single-region deployment for SaaS | One region outage = full downtime | Multi-region with Cloud Load Balancing |
| BigQuery on-demand for heavy workloads | Unpredictable costs at scale | Use BigQuery slots (flat-rate) for consistent workloads |
| Running Cloud Functions for long tasks | 9-minute timeout, cold starts | Use Cloud Run for tasks > 60 seconds |
| Skill | Relationship |
|---|---|
engineering-team/aws-solution-architect | AWS equivalent — same 6-step workflow, different services |
engineering-team/azure-cloud-architect | Azure equivalent — completes the cloud trifecta |
engineering-team/senior-devops | Broader DevOps scope — pipelines, monitoring, containerization |
engineering/terraform-patterns | IaC implementation — use for Terraform modules targeting GCP |
engineering/ci-cd-pipeline-builder | Pipeline construction — automates Cloud Build and deployment |
| Document | Contents |
|---|---|
references/architecture_patterns.md | 6 patterns: serverless, GKE microservices, three-tier, data pipeline, ML platform, multi-region |
references/service_selection.md | Decision matrices for compute, database, storage, messaging |
references/best_practices.md | Naming, 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
SKILL.md and 6 other files (scripts, references) in engineering-team/skills/gcp-cloud-architect of alirezarezvani/claude-skills.
Open the folder on GitHubat commit 19392f7
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| GCP Cloud Architect this skillalirezarezvani/claude-skills | 28k | — | ~3.2k | Automated safety check: Pass | MIT | |
| Deploying On GCPancoleman/ai-design-components | 526 | — | ~3.9k | Automated safety check: Pass | MIT | |
| Gke Cost Analysisgoogle/skills | 21k | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Cloud Monitoring Metric Selectiongoogle/skills | 21k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| GCP DrawIO Diagram Generatora5c-ai/babysitter | 1.8k | — | ~3.7k | Automated safety check: Pass | MIT | |
| Dd GCP Integrationdatadog-labs/agent-skills | 177 | — | ~8k | Automated safety check: Notes | MIT |
ancoleman/ai-design-components
Implement applications using Google Cloud Platform (GCP) services.
google/skills
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics.
google/skills
Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud…
a5c-ai/babysitter
Creates DrawIO XML diagrams of Google Cloud architectures from text or images, and analyzes existing .drawio files to list their GCP components.
datadog-labs/agent-skills
Set up the Datadog Google Cloud integration with Terraform - creates a service account in the host project, lets Datadog's delegate principal impersonate it via roles/iam.serviceAccountTokenCreator…
google/skills
Assists in designing and implementing secure n-tier serverless web applications and microservices on Google Cloud.
alirezarezvani/claude-skills
Writes INVEST-checked user stories with acceptance criteria, splits epics, plans sprints from velocity and ranks the backlog with a weighted score.
alirezarezvani/claude-skills
OKR cascade toolkit for product leaders: generates aligned company-to-team OKRs from five strategy types and scores how well they line up.
alirezarezvani/claude-skills
App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store.
alirezarezvani/claude-skills
Design AWS architectures for startups using serverless patterns and IaC templates.
alirezarezvani/claude-skills
Calculates attribution, funnel and ROI figures for marketing campaigns with three Python scripts that need only the standard library.
alirezarezvani/claude-skills
Reverse-engineers a frontend, backend or fullstack codebase into a product requirements document with per-page docs, an enum dictionary and an API inventory.
Categories
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.
GCP Cloud Architect fits situations like: asked to design Google Cloud infrastructure; configure BigQuery pipelines; optimize GCP costs.
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.
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.
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.
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.
SKILL.md names 1 domain. As links in the text: cloud.google.com. This is read from the text; nothing was executed.
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.
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.
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.
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.
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.