agent-platform-deploy

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: agent-platform-deploy
metadata:
  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 public Vertex AI
  deployments (use the `vertex-deploy`
  skill) or for running model evaluations (use the `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.

## 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 confirmation prompt to the user explaining the proposed command.
        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.

## 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:

```bash
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.

```bash
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:

```bash
gcloud ai model-garden models list-deployment-config \
    --model="<PUBLISHER>/<F