Gcp Gke
- 38 installs
- 6 repo stars
- Updated March 13, 2026
- alphaonedev/openclaw-graph
gcp-gke is a Claude Code skill that provides architecture guidance and cost modeling for deploying containerized applications on Google Kubernetes Engine.
About
gcp-gke is a skill that gives an agent architecture guidance, cost modeling, and pre-flight IAM requirements for running containerized applications on Google Kubernetes Engine. It delegates all actual provisioning to the official Google Cloud Python client libraries. A developer consults it when selecting GCP services, estimating GKE cost, or setting up Workload Identity Federation.
- Architecture and cost-modeling guidance for running containers on Google Kubernetes Engine
- Delegates provisioning to the official Google Cloud Python SDK
- Covers IAM / Workload Identity Federation pre-flight requirements
Gcp Gke by the numbers
- 38 all-time installs (skills.sh)
- +5 installs in the week ending Jun 23, 2026 (Skillselion tracking)
- Ranked #765 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
- Data as of Jul 7, 2026 (Skillselion catalog sync)
gcp-gke capabilities & compatibility
Requires a Google Cloud account and GCP billing; incurs standard GKE/GCP usage cost
- Capabilities
- gcp storage · gcp terraform · gcp vertexai
- Works with
- gcp · kubernetes · terraform
- Use cases
- devops · ci cd
- Runs
- Runs locally
- Pricing
- Bring your own API key
What gcp-gke says it does
Manage and deploy containerized applications on Google Kubernetes Engine for scalable cloud-native orchestration.
This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.
Pre-flight IAM / Workload Identity Federation requirements
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| Installs | 38 |
|---|---|
| repo stars | ★ 6 |
| Last updated | March 13, 2026 |
| Repository | alphaonedev/openclaw-graph ↗ |
What it does
Consult it when architecting or provisioning containerized workloads on Google Kubernetes Engine.
Who is it for?
GCP service selection, GKE cost estimation, and IAM/Workload Identity pre-flight planning
When should I use this skill?
planning or provisioning containerized workloads on Google Kubernetes Engine
By the numbers
- Lists 5 GCP service client libraries to install (bigquery, storage, pubsub, run, aiplatform)
Files
gcp-gke
Google Cloud Integration
This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.
# Core GCP client library
pip install google-cloud-python
# Vertex AI + Agent Engine (AI/ML workloads)
pip install google-cloud-aiplatform
# Specific service clients (install only what you need)
pip install google-cloud-bigquery # BigQuery
pip install google-cloud-storage # Cloud Storage
pip install google-cloud-pubsub # Pub/Sub
pip install google-cloud-run # Cloud RunSDK Docs: https://github.com/googleapis/google-cloud-python Vertex AI SDK: https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk
Use the Google Cloud Python SDK for all GCP provisioning and operational actions. This skill provides architecture guidance, cost modeling, and pre-flight requirements — the SDK handles execution.
Architecture Guidance
Consult this skill for:
- GCP service selection and trade-off analysis
- Cost estimation and optimization (committed use discounts, sustained use)
- Pre-flight IAM / Workload Identity Federation requirements
- IaC approach (Terraform AzureRM vs Deployment Manager vs Config Connector)
- Integration patterns with Google Workspace and other GCP services
- Vertex AI Agent Engine for multi-agent workflow design
Agent & AI Capabilities
| Capability | Tool |
|---|---|
| LLM agents | Vertex AI Agent Engine |
| Model serving | Vertex AI Model Garden |
| RAG | Vertex AI Search + Embeddings API |
| Multi-agent | Agent Development Kit (google/adk-python) |
| MCP | Vertex AI Extensions (MCP-compatible) |
Reference
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