/agent-platform-deploy

name: agent-platform-deploy
metadata:
  version: "1.0.5"
  category: AiAndMachineLearning
description: >-
  Deploy open models or custom weights from Model Garden to Agent Platform
  endpoints, check the status of an in-progress deployment operation, or clean
  up resources by undeploying models and deleting endpoints. Use when asked to
  actively deploy a model, list the Model Garden CATALOG of available models,
  check if a specific model is deployable
  (`gcloud ai model-garden models list-deployment-config`), query deployment
  cost, troubleshoot deployment errors (like quota limits), or undeploy/clean
  up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't
  use for pure listing/discovery questions of the form "is X deployed?",
  "list my endpoints", or "which regions have models running?" — for those
  use `agent-platform-endpoint-management`. Don't use for running model evaluations
  (use `agent-platform-eval-flywheel` skill).

Agent Platform Model Garden Deploy Skill

This skill provides instructions for deploying Open Models from Agent Platform Model Garden to endpoints, and subsequently undeploying them to clean up resources.

Attribution

**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you MUST read [Usage Attribution](references/attribution.md). For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix `gcloud` with `CLOUDSDK_METRICS_REQUEST_ATTRIBUTION` and append the attribution suffix to `User-Agent` headers:

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:agent-platform-deploy)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:agent-platform-deploy)

1P Tuned Model Copy & Deployment

If you need to copy a **1P (First-Party) Tuned Model** from a source project to a destination region or project and deploy it to a newly created endpoint, refer to the [1P Tuned Model Copy & Deployment Guide](references/copy_deploy_guide.md).

Safety & Confirmation Tiers (CRITICAL)

Before executing any commands on behalf of the user, you MUST adhere to the following safety tiers based on the action requested:

1. **Tier R: Read-only (`list`, `describe`, `list-deployment-config`)**

• **Rule**: No confirmation needed. You may execute these commands

immediately to gather information for the user. 2. **Tier M: Mutating & Reversible (`deploy`, `undeploy-model`)**

• **Rule**: This requires explicit user confirmation. You MUST present a

clear dry-run confirmation card containing:

1. Exact proposed `gcloud` command code block (`gcloud ai model-garden models deploy ... --asynchronous`). 2. Model identifier, destination project ID, and target region. 3. Machine type and accelerator configuration. 4. Estimated hourly cost ($/hr). 5. Endpoint display name. 6. Explicit confirmation prompt asking the user to approve before execution. You MUST wait for their explicit confirmation before executing. For `undeploy-model`, you MUST first verify that the endpoint and deployed model exist; if `describe` or `list` returns a 404 or empty result, you MUST halt and inform the user rather than attempting undeployment.

• **Same-turn restriction**: Do not run the command in the same turn as

presenting the confirmation prompt. End your turn after asking and wait for the user's reply; only execute after explicit approval. Printing a preview and then calling the tool before the user can answer does not count as obtaining confirmation. 3. **Tier D: Destructive & Irreversible (`delete`)**

• **Rule**: This requires **explicit typed confirmation**. You MUST output

a text message explaining the irreversible nature of endpoint or model deletion and asking the user to type "I confirm" or "Yes, delete it" before executing the deletion command.

> [!IMPORTANT] > > **Always Output Complete Text Response (NEVER Emit Empty Text)**: After > executing any tool call (such as `gcloud ai model-garden models deploy`, > `gcloud ai endpoints delete`, `gcloud ai endpoints list`, or status checks), > you MUST formulate and return a complete, informative textual response to the > user. Explicitly report the operation name, endpoint name/ID, error message, > or list of resources. **NEVER finish a turn with empty text or silence**.

1. Prerequisites

Before deploying, ensure you have the correct project and region set. The commands below use placeholder variables `PROJECT_ID` and `LOCATION_ID`.

Ensure you are authenticated:

gcloud auth login
gcloud auth application-default login
gcloud config set project $PROJECT_ID

2. Discovering Deployable Models

You can list models available in Model Garden and check if they can be self-deployed.

gcloud ai model-garden models list

To see what machine types and accelerators are supported for a specific model, pass a `MODEL_ID` you obtained from the `models list` output above. Substitute `<PUBLISHER>/<FAMILY>@<VERSION-ID>` below with the exact string from the catalog output — the placeholder is deliberately not a real model ID:

gcloud ai model-garden models list-deployment-config \
    --model="<PUBLISHER>/<FAMILY>@<VERSION-ID>"

> [!NOTE] Some models, especially Hugging Face models, might require a Hugging > Face Access Token for deployment.

> [!TIP] **Model Recommendation Instructions:** Whenever you are about to name a > specific model version in a response, do NOT recommend from memory. This > applies in all of the following situations — not just direct deploy requests: > > * The user asks to deploy a model without naming one. > * You are volunteering a next-step suggestion after a `list`, `describe`, or > `undeploy` operation (e.g. "Would you like me to deploy `<model>` to this > endpoint?").