Agent Platform Model Registry

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Tool integration
Data and AI
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Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to managing machine learning models in the Agent Platform Model Registry. It supports listing, describing, uploading, updating, and deleting models. The skill enforces strict safety protocols, including mandatory interactive confirmation for mutating operations and explicit typed confirmation for destructive actions, ensuring controlled management of model metadata and artifacts.

What it does

Lists models and retrieves detailed metadata for specific model versions.
Uploads new models or registers new versions of existing models.
Updates model metadata such as display names and descriptions.
Deletes models and their associated versions from the registry.
Searches for publisher models within the Model Garden.
Enforces usage attribution for all executed commands.

When to use it

When you need to catalog or organize machine learning models.
When updating metadata for an existing model in the registry.
When registering a new model or a new version of an existing model.
When cleaning up the registry by removing obsolete models.
When you need to verify model details before performing operations.

Advantages

Provides clear safety tiers for read, update, and delete operations.
Prevents accidental deletions through mandatory typed confirmation.
Ensures consistent parameter handling for regional and project-specific commands.
Includes built-in attribution for tracking command usage.
Standardizes the process for updating model metadata via Python SDK.

Usage examples

“List the 50 most recent models in my project.”: The agent executes a gcloud command to list the 50 most recently updated models in the specified region and project.
“Delete the model with ID 12345.”: The agent requests explicit typed confirmation from the user before proceeding with the irreversible deletion of the specified model.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google/agent-platform-model-registry/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip agent-platform-model-registry.zip -d .agents/skills

Documentation by Google

Agent Platform Model Registry Management

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. 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-model-registry)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:agent-platform-model-registry)

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, --project=..., --display-name="...") — natural-language paraphrases are NOT sufficient.
- Same-turn restriction: NEVER execute the command in the same turn as receiving the request or presenting the confirmation prompt! In Turn 1, you MUST ONLY present the interactive confirmation card with the exact, literal command string. Stop and wait for the user's reply; only execute in the subsequent turn after explicit 'Yes' / approval. Executing upload or update in Turn 1 without prior confirmation is strictly prohibited.
- Mid-flow parameter changes / rejection: If the user rejects the prompt or changes any parameters (e.g., display name, description, parent model), do NOT execute the old command. Adapt immediately and present a NEW confirmation prompt with the updated literal command and wait for 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.
- Mid-flow target changes: If the user changes their mind (e.g., "delete the second model instead"), do NOT delete the first model. Present a fresh typed confirmation prompt for the newly selected model ID and wait for approval.
Cost Estimation: Model Registry operations manage catalog metadata and stored model artifacts without provisioning serving compute or endpoints. Do NOT call the estimate_cost tool for Model Registry actions, as estimate_cost is designed for serving infrastructure (endpoints/batch prediction) and will return an error if called for registry operations. If including cost in the preview card, state that Model Registry operations incur no serving compute charges ($0.00 compute charges; standard Cloud Storage pricing applies to model artifacts).

Phase 0: Environment Setup & Parameter Resolution

CRITICAL: Before running any commands, verify that all necessary parameters are known:

Missing Region or Project: Follow the base environment grounding policy: if a session location or project is already set from prior turns, reuse it without re-asking. If missing from both prompt and session context, at most one direct lookup is permitted (e.g. gcloud config get project or gcloud config get compute/region). If the user refers to "my project" or their configured environment and compute/region is (unset), fall back to us-central1 per base environment grounding; otherwise, if the region is still unresolved, pause and explicitly ask the user for the missing parameter before executing regional commands.
Missing Model ID: If the user asks to update or describe a model without providing the model ID, pause and ask the user for the model ID, or offer to list models first to help them find it.
Placeholder Substitution: If the user's requested display name contains a placeholder token (e.g., <unique-suffix>, [suffix], or <timestamp>), generate a short unique alphanumeric string or timestamp and substitute it cleanly. Never pass unexpanded literal placeholder tokens to the API.
Region and Project Flags: Always pass --region=$LOCATION_ID and --project=$PROJECT_ID explicitly on all gcloud ai models commands. 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.

Always pass --limit. A project can hold thousands of models, and an unbounded list pages through every one of them, which can take over a minute and return hundreds of KB of output. Results come back most recently updated first, so --limit=50 returns the newest models in a few seconds.

gcloud ai models list \
    --region=$LOCATION_ID \
    --project=$PROJECT_ID \
    --limit=50

Keep --limit=50 when the user asks to list "all" models, and say the reply shows the 50 most recently updated models. Do NOT page through the whole registry (with gcloud or Model.list()) unless the user asks for a count or the complete inventory; offer to look up a specific model by display name instead.
If the user pushes back and asks for the complete inventory, drop --limit and print one compact line per model with --format="value(name.basename(),displayName)". Warn that this can take a minute or more in a large project.
If the user asks how many models there are, count the IDs without printing the list. There is no count API, so this still pages through every model (about a minute per 1,500 models); tell the user it may take a while. Do not run a --limit list first.
```
gcloud ai models list \
    --region=$LOCATION_ID \
    --project=$PROJECT_ID \
    --format="value(name)" | wc -l
```
Do NOT use --filter or --sort-by to narrow the list. gcloud applies both client-side after fetching every page, so they are as slow as an unbounded list.
To find a model by display name (e.g. to confirm an upload or deletion), filter on the server with the Python SDK:

python3 - <<'PY'
from google.cloud import aiplatform

aiplatform.init(project='<PROJECT_ID>', location='<LOCATION_ID>')
for m in aiplatform.Model.list(filter='display_name="<DISPLAY_NAME>"'):
    print(m.name, m.display_name, m.create_time)
PY

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 \
    --project=$PROJECT_ID

To target a specific version:

gcloud ai models describe ${MODEL_ID}@${VERSION_ID} \
    --region=$LOCATION_ID \
    --project=$PROJECT_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=$LOCATION_ID \
    --project=$PROJECT_ID \
    --display-name="<DISPLAY_NAME>" \
    --container-image-uri="<CONTAINER_IMAGE_URI>" \
    [--artifact-uri="<ARTIFACT_URI>"]

[!IMPORTANT]
This is a Tier M operation — see [Safety & Confirmation Tiers] above.
If the user specifies "with no artifact URI", omit --artifact-uri.
If registering a new version of an existing model, include --parent-model=$PARENT_MODEL_ID.
Substitute <DISPLAY_NAME> with the exact name requested by the user.

4. Updating a Model (Tier M)

Update metadata fields like display name or description. Note that gcloud ai models does NOT have an update subcommand. Instead, model metadata updates MUST be executed using the Vertex AI Python SDK (google.cloud.aiplatform.Model).

Action requires an inline confirmation card containing the exact script before proceeding.

python3 -c "
from google.cloud import aiplatform

aiplatform.init(project='$PROJECT_ID', location='$LOCATION_ID')
model = aiplatform.Model('$MODEL_ID')
model.update(display_name='<NEW_DISPLAY_NAME>', description='<NEW_DESCRIPTION>')
print(f'Successfully updated model: {model.resource_name}')
"

[!IMPORTANT]
This is a Tier M operation — see [Safety & Confirmation Tiers] above.
If only updating the display name, pass model.update(display_name='<NEW_DISPLAY_NAME>').
If only updating the description, pass model.update(description='<NEW_DESCRIPTION>').
The confirmation card MUST display the exact python command snippet above. NEVER execute in Turn 1; wait for explicit user approval.

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 \
    --project=$PROJECT_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.

Files

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LICENSELicence file added by AutoGineer11.1 KiB
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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Python

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Attribution and licence

Published by

Google

Publisher

Google (Official)

Source

google/skills · skills/cloud/agent-platform-model-registry · commit 2194d3b

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Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
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2026-11-08

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