agent-platform-tuning-management — Claude Code skill for tuning jobs

---
name: agent-platform-tuning-management
metadata:
  version: "1.0.1"
  category: AiAndMachineLearning
description: >-
  Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel
  ongoing model tuning jobs. Don't use for fine-tuning models (use
  `agent-platform-tuning`), deploying models to endpoints (use
  `agent-platform-deploy`), or managing serving endpoints (use
  `agent-platform-endpoint-management`).
---

# Agent Platform Tuning Management

This skill provides instructions on how to manage GenAI Tuning Jobs using the
Agent Platform Python SDK. Use this skill when a user wants to check the status
of their tuning runs, find an active tuning job, or cancel a job that is running
too long.

## 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`, `get`)**
    *   **Rule**: No confirmation needed. You may execute these commands
        immediately to gather information for the user.
2.  **Tier D: Destructive & Interruptive (`cancel`)**
    *   **Rule**: Cancellation is a Tier D action requiring **explicit typed
        confirmation** (e.g. "I confirm" or "Yes, cancel it").
    *   **Required Fields in Dry-Run Confirmation Card**: Before cancelling a
        tuning job, you MUST present a dry-run confirmation preview clearly
        listing:
        *   **Target Resource**: The full tuning job resource name or ID (e.g.
            `projects/<PROJECT_ID>/locations/<REGION>/tuningJobs/<JOB_ID>`).
        *   **Command / Script**: The exact cancellation command or Python code
            to be executed.
        *   **Expected Effect**: Stops the ongoing tuning job; any in-progress
            training will be halted and cannot be resumed.
        *   Ask the user to explicitly confirm (e.g., "Do you confirm? Please
            reply with 'I confirm' or 'Yes, cancel it'.").
    *   **Same-turn restriction**: NEVER execute the cancellation in the same
        turn as presenting the preview card. Stop immediately and wait for the
        user to confirm in a new turn. Even if the user provided pre-emptive
        confirmation (e.g. "Yes, I confirm, cancel tuning job ...") or provides
        a corrected job ID, you MUST present the dry-run preview for that
        specific job ID and wait for confirmation in a separate turn before
        issuing the cancellation.

## Phase 0: Environment Setup

**CRITICAL**: Before running any of the Python snippets below, you MUST ensure
the environment is correctly initialized by following these steps:

1.  **Google Cloud Authentication**: Authenticate with your Google Cloud account
    and configure active Application Default Credentials (ADC) for Agent
    Platform access:

    ```bash
    gcloud auth login
    gcloud auth application-default login
    ```

2.  **Python Dependencies**: This skill needs `google-cloud-aiplatform`. Do
    **not** create a virtual environment — it starts empty and hides packages
    the environment already provides, forcing a redundant install. Probe, and
    install only what is missing:

    ```bash
    python3 -c "import vertexai" || pip install google-cloud-aiplatform
    ```

3.  **Execution**: Run Python snippets with a plain `python3`. There is no
    environment to activate first.

## Workflow Decision Tree

1.  **Information Gathering**: Do you have a Project ID and Region?

    *   **No** -> You **MUST** ask the user for the missing Project ID and
        Region in plain text, or advise them to check their gcloud
        configuration. If neither location has this information, then ask the
        user to provide it. Do not attempt to search random regions on your own.
    *   **Yes** -> Proceed to Step 2.

2.  **Task Type**: What does the user want to do?

    *   **Find or List Jobs** -> Use the Python SDK to list tuning jobs. (Tier
        R)
    *   **Check Status / Inspect a Specific Job** -> Use the Python SDK to get
        tuning job details. (Tier R)
    *   **Cancel a Job** -> Ask for confirmation, then use the Python SDK to
        cancel the tuning job. (Tier D)

## Using the Python SDK

> [!NOTE]
>
> **Resource Verification & Missing Projects/Jobs:** If the execution of the
> Python snippet fails with an error (such as `403 Permission Denied`, `404 Not
> Found`, `INVALID_ARGUMENT`, or indicating a dummy/missing project or job ID),
> you **MUST** inform the user that the project or tuning job does not exist or
> cannot be accessed. You **MUST** prompt the user to provide a valid Project ID
> or Job ID, and stop tool execution immediately to wait for their response. Do
> **NOT** retry or loop, do **NOT** assume the resource is valid, and do **NOT**
> execute further scripts before receiving valid details from the user.

### 1. Listing Tuning Jobs (Tier R)

If the user asks "What tuning jobs do I have running?" or wants to find a
specific job ID:

```python
from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
parent = f"projects/{project_id}/locations/{region}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

jobs = client.list_tuning_jobs(parent=parent)
for job in jobs:
    print(f"Name: {job.name}")
    print(f"Base Model: {job.base_model}")
    print(f"State: {job.state}")
```

`list_tuning_jobs` takes no `page_size` keyword. To set one, pass the request:
`client.list_tuning_jobs(request={"parent": parent, "page_size": 50})`.

### 2. Getting Details for a Specific Job (Tier R)

If the user provides a Tuning Job ID and asks for its status:

```python
from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
job_id = "YOUR_JOB_ID"  # 19-digit ID
name = f"projects/{project_id}/locations/{region}/tuningJobs/{job_id}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

job = client.get_tuning_job(name=name)
print(f"Name: {job.name}")
print(f"Base Model: {job.base_model}")
print(f"State: {job.state}")
print(f"Tuning Model: {job.tuned_model_display_name}")
```

### 3. Canceling a Job (Tier D)

If the user explicitly requests to stop, abort, or cancel a running tuning job:

**Safety Check**: **Action requires explicit typed confirmation before
proceeding.** You MUST present a dry-run confirmation card listing the Target
Resource, Command/Script, and Expected Effect, and ask the user to type "I
confirm" or "Yes, cancel it". Even if the user provided confirming language
pre-emptively or is providing a corrected/new job ID, you MUST present the
preview card for that specific job ID and wait for their explicit approval in a
new turn.

> [!IMPORTANT]
>
> **NEVER pre-emptively execute any cancellation code or command before
> receiving the user's response in a new turn.** You must never speculate or
> assume that confirmation will be given. Executing cancellation in the same
> turn as presenting the preview card is a severe safety violation.

```python
from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
job_id = "YOUR_JOB_ID"  # 19-digit ID
name = f"projects/{project_id}/locations/{region}/tuningJobs/{job_id}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

client.cancel_tuning_job(name=name)
print(f"Successfully requested cancellation for {name}")
```