Agent Platform Model Registry Agent Skill

Agent Platform Model Registry Management

Overview

This skill provides instructions for managing machine learning models in the

Agent Platform Model Registry. It covers listing models, describing model

details, uploading new models or versions, updating metadata, and deleting

models.

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:

• Tier R: Read-only (list, describe, get)
• No confirmation needed. Execute immediately to gather information.
• Tier M: Mutating & Reversible (upload, update)
• Requires interactive confirmation with 'Yes'/'No' options. The

confirmation prompt MUST contain the exact, literal command string with

all required flags (e.g. --region=us-central1, --display-name="...")

— natural-language paraphrases are NOT sufficient.

• Same-turn restriction: NEVER execute the command in the same turn as

presenting the confirmation prompt. Stop and wait for the user's reply;

only execute after explicit 'Yes' / approval.

• Tier D: Destructive & Irreversible (delete)
• Requires explicit typed confirmation (e.g. "I confirm" or "Yes,

delete it"). Ask for confirmation IMMEDIATELY — before any pre-flight

checks (don't check if the model is deployed to endpoints first).

• Same-turn restriction: NEVER execute in the same turn as asking for

typed confirmation. Wait for the user to reply in a new turn.

Phase 0: Environment Setup

CRITICAL: Before running any commands, you MUST ensure the environment is

correctly initialized by following these steps:

• Google Cloud Authentication: Authenticate with your Google Cloud

credentials and configure active Application Default Credentials (ADC) for

Agent Platform access:

    gcloud auth login
    gcloud auth application-default login

• Set Project: Configure the active project for subsequent commands:

    gcloud config set project $PROJECT_ID

• Region: Always specify --region=$LOCATION_ID on each command below. Do

NOT use global.

1. Listing Models (Tier R)

Use this command to discover existing models in the registry and retrieve their

numeric IDs. No confirmation is required.

gcloud ai models list \
    --region=$LOCATION_ID

2. Describing a Model (Tier R)

Retrieve the full metadata for a specific model or version. No confirmation is

required.

gcloud ai models describe $MODEL_ID \
    --region=$LOCATION_ID

To target a specific version:

gcloud ai models describe ${MODEL_ID}@${VERSION_ID} \
    --region=$LOCATION_ID

3. Uploading a Model (Tier M)

Register a new model or a new version of an existing model. This is a

long-running operation. **Action requires an inline confirmation card before

proceeding.**

Example: Uploading a Custom Model

gcloud ai models upload \
    --region=$LOCATION_ID \
    --display-name="my-custom-model" \
    --container-image-uri="gcr.io/my-project/my-model:latest" \
    --artifact-uri="gs://my-bucket/path/to/artifacts"

> [!IMPORTANT]

>

> This is a Tier M operation — see [Safety & Confirmation Tiers] above.

To upload a new version of an existing model, use the --parent-model flag or

specify the parent model ID.

4. Updating a Model (Tier M)

Update metadata fields like display name, description, or labels. **Action

requires an inline confirmation card before proceeding.**

gcloud ai models update $MODEL_ID \
    --region=$LOCATION_ID \
    --display-name="new-display-name" \
    --description="Updated description"

> [!IMPORTANT]

>

> This is a Tier M operation — see [Safety & Confirmation Tiers] above.

5. Deleting a Model (Tier D)

Permanently delete a Model and all its versions. **Action requires explicit

typed confirmation before proceeding.**

gcloud ai models delete $MODEL_ID \
    --region=$LOCATION_ID

> [!WARNING]

>

> This operation is irreversible. All model versions must be undeployed from all

> Endpoints before deletion.

6. Searching Publisher Models (Tier R)

Before generating interactive model details, you MUST verify the model_id by

searching Model Garden Publisher Models. No confirmation is required.

Use the gcloud ai CLI to search for matching publisher models.

gcloud ai model-garden models list --model-filter="<model_name_or_query>" --full-resource-name --format=json

This will return a list of matching models. Extract the exact name field from

the result (e.g., publishers/google/models/gemma2 or

publishers/qwen/models/qwen3-coder) to use as the verified model_id.