Gcp Functions
- 48 installs
- 6 repo stars
- Updated March 13, 2026
- alphaonedev/openclaw-graph
gcp-functions is a skill that provides architecture guidance and cost modeling for Google Cloud Functions while delegating deployment to the Google Cloud Python client.
About
This skill provides architecture guidance, cost modeling, and pre-flight IAM requirements for Google Cloud Functions, a serverless platform for event-driven applications. A developer uses it to plan serverless function deployment and triggers while the official Google Cloud Python client handles execution. It also covers Vertex AI integration patterns for AI workloads.
- Architecture guidance for event-driven Cloud Functions
- Delegates execution to Google Cloud Python client
- Cost modeling and IAM pre-flight requirements
Gcp Functions by the numbers
- 48 all-time installs (skills.sh)
- Ranked #730 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
gcp-functions capabilities & compatibility
Free guidance; GCP billing applies to Cloud Functions usage via the SDK
- Capabilities
- architecture guidance · cost modeling · iam preflight · function deploy
- Works with
- gcp
- Use cases
- devops · ci cd
- Runs
- Hosted SaaS
- Pricing
- Bring your own API key
What gcp-functions says it does
Deploy and manage serverless functions on Google Cloud Platform using Cloud Functions for event-driven 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 | 48 |
|---|---|
| repo stars | ★ 6 |
| Last updated | March 13, 2026 |
| Repository | alphaonedev/openclaw-graph ↗ |
What it does
Plan serverless Cloud Functions deployment and event triggers with cost and IAM guidance while the SDK executes.
Who is it for?
Planning event-driven serverless function architecture and cost before executing with the SDK.
Skip if: Long-running stateful services or non-GCP serverless platforms.
When should I use this skill?
You need to deploy event-driven serverless functions and want architecture and cost guidance first.
What you get
A Cloud Functions deployment plan with cost model and IAM pre-flight, executed via the Python SDK.
- Function architecture guidance
- Cost estimates
- IAM pre-flight requirements
Files
gcp-functions
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 functions directly?
No, it delegates provisioning to the official Google Cloud Python client and provides architecture and cost guidance.
What are Cloud Functions for?
Deploying and managing serverless functions on GCP for event-driven applications.