Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
Generates a LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini LiveAPI websocket endpoint (Gemini Enterprise or non-Gemini Enterprise), handles session setup/resumption, bearer token refresh, and…
Evaluate and improve GenAI models and agents using the Google GenAI Evaluation SDK. Creates eval datasets (from session traces or synthetic generation), selects and configures metrics (RubricMetric, LLMMetric, CodeExecutionMetric), executes evals via client.evals.evaluate(), and analyzes results to suggest concrete…
Primary Router for Vertex AI skills. Use this skill when the user wants to work with Google Cloud Vertex AI (e.g., deploying models, running inference, or tuning models). This skill routes to vertex-deploy, vertex-inference, or vertex-tuning.
Skill "vertex-deploy" from GoogleCloudPlatform/vertex-ai-samples, covering vertex ai model garden deploy skill, 1. prerequisites, 2. discovering deployable models, 3. deploying a model and example: deploying gemma 3.
Skill "vertex-inference" from GoogleCloudPlatform/vertex-ai-samples, covering vertex ai genai inference skill, 1. authentication (critical), 2. gemini models, choosing the right sdk and installation.
Skill "open-model" from GoogleCloudPlatform/vertex-ai-samples, covering vertex ai open model tuning, workflow decision tree, phase 0: environment & iam setup {#phase-0}, 0.1 authentication & project context and 0.2 possible locations.