getting-started

AI/ML Skills on Google Cloud: Complete Guide to the Google Cloud Skills Repository

September 4, 2026 google/skills ↗

The Google Cloud Skills repository contains modular, self-contained SKILL definition files that automate deployment, troubleshooting, and code generation for AI/ML workloads across GKE, Vertex AI, and Gemini APIs.

The google/skills repository hosts a comprehensive collection of automation capabilities organized as SKILL.md files and supporting reference documentation. These AI/ML skills on Google Cloud cover the entire machine learning lifecycle—from GPU cluster upgrades and TPU troubleshooting to generative AI application development using GenKit and the Vertex AI Agent Platform.

Model Inference and Serving on GKE

The gke-inference skill, defined in /skills/cloud/gke-inference/SKILL.md, automates the deployment and optimization of AI/ML inference workloads on Google Kubernetes Engine (GKE). This skill supports GPU, TPU, and CPU-only node configurations, generating deployment manifests and returning service endpoints upon completion.

Reference material for CLI commands is available in references/cli-reference.md within the skill directory.

Cluster Management for Large-Scale AI/ML

For organizations running heavy GPU or TPU workloads, the gke-upgrades skill at /skills/cloud/gke-upgrades/SKILL.md provides upgrade-time best practices specifically tailored for large-scale AI/ML clusters. This ensures minimal disruption to training jobs and inference services during cluster maintenance windows.

AI/ML Training Troubleshooting

The repository includes four specialized skills for diagnosing and recovering from training job failures:

Generative AI Frameworks (GenKit)

GenKit skills provide end-to-end libraries for building, prompting, and evaluating generative AI applications across multiple programming languages:

These skills abstract interactions with Gemini models, Vertex AI, and evaluation frameworks, enabling consistent implementation regardless of language choice.

Gemini Generative AI APIs

For direct model interaction, three distinct API skills are available:

Vertex AI Agent Platform

The Vertex AI Agent Platform skills manage the complete lifecycle of Retrieval-Augmented Generation (RAG) agents:

Storage Optimized for AI/ML Workloads

High-performance storage configuration is critical for model checkpoints and dataset access:

Developer Knowledge and Documentation Access

The retrieving-developer-knowledge skill (/skills/developers/retrieving-developer-knowledge/SKILL.md) provides programmatic access to official AI/ML documentation from ai.google.dev, ADK, and TensorFlow via MCP server or REST fallback (reference: references/supported-domains.md).

Practical Implementation Examples

Below are executable snippets demonstrating how to invoke these AI/ML skills within the Google Cloud Developer Assistant environment.

Deploying AI/ML Inference on GKE

{
  "skill": "gke-inference",
  "args": {
    "project_id": "my-gcp-project",
    "region": "us-central1",
    "model_name": "gemini-1.5-flash-001",
    "hardware": "GPU"
  }
}

This payload creates a GKE cluster if needed, builds the deployment manifest, and returns the service URL for model serving.

Running GenKit Python Generation

import genkit as gk
import genkit.plugins.vertexai as vertex

vertex.configure(project="my-gcp-project", location="us-central1")
prompt = "Summarize the latest research on transformer scaling laws."
response = gk.run(vertex.generate(prompt=prompt, model="gemini-1.5-flash-001"))
print(response.text)

The GenKit Python skill streams the response from the Vertex AI Gemini endpoint and returns structured output.

Creating a RAG Engine with Vertex AI

import vertexai
from vertexai.preview import rag

vertexai.init(project="my-gcp-project", location="us-central1")
rag_engine = rag.RagEngine(
    name="my-rag-engine",
    vector_store=rag.VectorStore(name="my-vector-store"),
    retrieval_config=rag.RetrievalConfig(top_k=5)
)
rag_engine.create()
print(f"Engine created: {rag_engine.resource_name}")

This invokes the Agent-Platform RAG Engine Management skill to provision a retrieval engine linked to the specified vector store.

Summary

  • The google/skills repository organizes AI/ML capabilities into discrete, documented SKILL.md files covering GKE inference, training troubleshooting, GenKit frameworks, and Vertex AI management.
  • Infrastructure skills target GPU/TPU cluster upgrades (gke-upgrades) and high-performance storage configuration (google-cloud-storage-bucket-architect).
  • Operational skills provide automated recovery for TPU OOM events and JobSet interruptions.
  • Development skills include multi-language GenKit bindings and comprehensive Vertex AI Agent Platform lifecycle management.
  • Each skill references specific implementation files (e.g., references/generation.md, references/create.md) for detailed command syntax and configuration options.

Frequently Asked Questions

How do I deploy a model inference service using the GKE skills?

Invoke the gke-inference skill by sending a JSON payload specifying your project ID, region, model name, and hardware type (GPU, TPU, or CPU) to the Google Cloud Developer Assistant. The skill automatically provisions GKE resources and returns the service endpoint URL.

What troubleshooting capabilities are available for TPU training failures?

The repository provides four dedicated TPU troubleshooting skills: metrics monitoring for hardware health, dynamic slice monitoring for resource allocation, JobSet interruption handling for distributed training recovery, and vBar OOM management for automatic recovery from memory exhaustion events.

How do the Vertex AI Agent Platform skills differ from the Gemini API skills?

Vertex AI Agent Platform skills (agent-platform-*) manage full RAG agent lifecycles including vector stores, prompt versioning, and deployment infrastructure, while Gemini API skills (gemini-api, gemini-interactions-api, gemini-live-api) provide low-level model interfaces for direct text generation, chat, and streaming without the RAG orchestration layer.

Where can I find setup instructions for the GenKit Python skill?

Configuration details for GenKit Python are located in references/setup.md within the /skills/cloud/genkit-python/ directory, covering Vertex AI project initialization, plugin installation, and environment configuration requirements.

Have a question about this repo?

These articles cover the highlights, but your codebase questions are specific. Give your agent direct access to the source. Share this with your agent to get started:

Share the following with your agent to get started:
curl -s "https://instagit.com/install.md"
Works with
Claude Codex Cursor VS Code OpenClaw Any MCP Client

Maintain an open-source project? Get it listed too →