Gcp Cloud Run
- 53 installs
- 6 repo stars
- Updated March 13, 2026
- alphaonedev/openclaw-graph
gcp-cloud-run is a skill that provides architecture guidance and cost modeling for deploying stateless containers on Google Cloud Run while delegating execution to the google-cloud-run Python client.
About
This skill provides architecture guidance, cost modeling, and pre-flight IAM requirements for Google Cloud Run, a serverless platform for deploying stateless containers. A developer uses it to plan how to deploy scalable containerized web applications while the official google-cloud-run Python client handles execution. It also covers Vertex AI integration patterns for AI workloads.
- Architecture guidance for deploying to Cloud Run
- Delegates execution to google-cloud-run Python client
- Cost modeling and IAM pre-flight requirements
Gcp Cloud Run by the numbers
- 53 all-time installs (skills.sh)
- Ranked #708 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
gcp-cloud-run capabilities & compatibility
Free guidance; GCP billing applies to Cloud Run usage via the SDK
- Capabilities
- architecture guidance · cost modeling · iam preflight · container deploy
- Works with
- gcp · docker
- Use cases
- devops · ci cd
- Runs
- Hosted SaaS
- Pricing
- Bring your own API key
What gcp-cloud-run says it does
Deploy and manage stateless containers on Google's serverless platform for scalable web applications.
This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.
This skill provides architecture guidance, cost modeling, and pre-flight requirements — the SDK handles execution.
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| Installs | 53 |
|---|---|
| repo stars | ★ 6 |
| Last updated | March 13, 2026 |
| Repository | alphaonedev/openclaw-graph ↗ |
What it does
Plan deployment of stateless containers on Cloud Run with cost and IAM guidance while the SDK handles execution.
Who is it for?
Planning Cloud Run deployment architecture, scaling, and cost before executing with the SDK.
Skip if: Stateful workloads or non-GCP container platforms.
When should I use this skill?
You need to deploy scalable stateless containers and want architecture and cost guidance first.
What you get
A Cloud Run deployment plan with cost model and IAM pre-flight, executed via the Python SDK.
- Deployment architecture guidance
- Cost estimates
- IAM pre-flight requirements
Files
gcp-cloud-run
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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Does this skill deploy to Cloud Run directly?
No, it delegates provisioning to the official google-cloud-run Python client and provides architecture and cost guidance.
What is Cloud Run used for?
Deploying and managing stateless containers on Google's serverless platform for scalable web applications.