Skip to content

Google Cloud

Verified

Deploy, monitor, and manage GCP services with battle-tested patterns.

923

Install

Claude Code

Add to .claude/skills/

About This Skill

# Google Cloud Production Rules

Cost Traps - Stopped Compute Engine VMs still pay for persistent disks and static IPs — delete disks or use snapshots for long-term storage - Cloud NAT charges per VM and per GB processed — use Private Google Access for GCP API traffic instead - BigQuery on-demand pricing charges for bytes scanned, not rows returned — partition tables and use `LIMIT` in dev, but `LIMIT` doesn't reduce scan cost in prod - Preemptible VMs save 80% but can be terminated anytime — only for fault-tolerant batch workloads - Egress to internet costs, egress to same region is free — keep resources in same region, use Cloud CDN for global distribution

Security Rules - Service accounts are both identity and resource — one service account can impersonate another with `roles/iam.serviceAccountTokenCreator` - IAM policy inheritance: Organization → Folder → Project → Resource — deny policies at org level override allows below - VPC Service Controls protect against data exfiltration — but break Cloud Console access if not configured with access levels - Default Compute Engine service account has Editor role — create dedicated service accounts with least privilege - Workload Identity Federation eliminates service account keys — use for GitHub Actions, GitLab CI, external workloads

Networking - VPC is global, subnets are regional — unlike AWS, single VPC can span all regions - Firewall rules are allow-only by default — implicit deny all ingress, allow all egress. Add explicit deny rules for egress control - Private Google Access is per-subnet setting — enable on every subnet that needs to reach GCP APIs without public IP - Cloud Load Balancer global vs regional — global for multi-region, but regional is simpler and cheaper for single region - Shared VPC separates network admin from project admin — host project owns network, service projects consume it

Performance - Cloud Functions gen1 has 9-minute timeout — gen2 (Cloud Run based) allows 60 minutes - Cloud SQL connection limits vary by instance size — use connection pooling or Cloud SQL Auth Proxy - Firestore/Datastore hotspotting on sequential IDs — use UUIDs or reverse timestamps for document IDs - GKE Autopilot simplifies but limits — no DaemonSets, no privileged containers, no host network - Cloud Storage single object limit is 5TB — use compose for larger, parallel uploads for faster

Monitoring - Cloud Logging retention: 30 days default, \_Required bucket is 400 days — create custom bucket with longer retention for compliance - Cloud Monitoring alert policies have 24-hour auto-close — incident disappears even if issue persists, configure notification channels for re-alert - Error Reporting groups by stack trace — same error with different messages creates duplicates - Cloud Trace sampling is automatic — may miss rare errors, increase sampling rate for debugging - Audit logs: Admin Activity always on, Data Access off by default — enable Data Access logs for security compliance

Infrastructure as Code - Terraform google provider requires project ID everywhere — use `google_project` data source or variables, never hardcode - `gcloud` commands are imperative — use Deployment Manager or Terraform for reproducible infra - Cloud Build triggers on push but IAM permissions on first run confusing — grant Cloud Build service account necessary roles before first deploy - Project deletion has 30-day recovery period — but project ID is globally unique forever, can't reuse - Labels propagate to billing — use consistent labeling for cost allocation: `env`, `team`, `service`

IAM Best Practices - Primitive roles (Owner/Editor/Viewer) are too broad — use predefined roles, create custom for least privilege - Service account keys are security liability — use Workload Identity, impersonation, or attached service accounts instead - `roles/iam.serviceAccountUser` lets you run as that SA — equivalent to having its permissions, grant carefully - Organization policies restrict what projects can do — `constraints/compute.vmExternalIpAccess` blocks public VMs org-wide

Use Cases

  • Manage Google Cloud Platform infrastructure and services programmatically
  • Provision GCP resources: Compute Engine, Cloud Storage, BigQuery, and Pub/Sub
  • Configure GCP security settings including IAM, encryption, and audit logging
  • Optimize GCP costs by identifying underutilized resources and recommending rightsizing
  • Automate GCP operational tasks: backups, scaling, and maintenance

Pros & Cons

Pros

  • + Broad GCP service coverage for infrastructure management
  • + Cost optimization guidance helps control cloud spending
  • + Security configuration support reduces misconfiguration risks

Cons

  • - GCP expertise required to validate AI-generated infrastructure recommendations
  • - Only available on claude-code and openclaw platforms
  • - Infrastructure changes have immediate production impact — requires careful review

Frequently Asked Questions

What does Google Cloud do?

Deploy, monitor, and manage GCP services with battle-tested patterns.

What platforms support Google Cloud?

Google Cloud is available on Claude Code, OpenClaw.

What are the use cases for Google Cloud?

Manage Google Cloud Platform infrastructure and services programmatically. Provision GCP resources: Compute Engine, Cloud Storage, BigQuery, and Pub/Sub. Configure GCP security settings including IAM, encryption, and audit logging.

Stay Updated on Agent Skills

Get weekly curated skills + safety alerts