/agent-platform-model-registry

name: agent-platform-model-registry
metadata:
  version: "1.0.2"
  category: AiAndMachineLearning
description: >-
  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.

Agent Platform Model Registry Management

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-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:

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

• No confirmation needed. Execute immediately to gather information.

2. **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. 3. **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. 4. **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:

1. **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. 2. **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. 3. **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. 4. **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 new