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Agent Platform Tuning Management

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Low risk

From the author

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`).
— 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 for interacting with GenAI tuning jobs within the Agent Platform. It enables users to list active jobs, inspect specific job details, and safely cancel ongoing training processes. The skill emphasizes secure execution practices, including mandatory attribution for API calls and a strict two-turn confirmation process for destructive actions like job cancellation.

What it does

  • Lists existing GenAI tuning jobs with optional filtering by state or display name.
  • Retrieves detailed status and configuration information for a specific tuning job.
  • Provides a safe, multi-turn workflow for cancelling active tuning jobs.
  • Includes safety checks to prevent accidental termination of jobs.
  • Requires explicit user confirmation before executing destructive cancellation commands.
  • Guides users through necessary environment setup and dependency verification.

When to use it

  • When you need to check the progress or status of a specific model tuning run.
  • When you want to identify all currently running or pending tuning jobs in a project.
  • When a tuning job is running longer than expected and needs to be aborted.
  • When you need to verify the configuration or base model of a specific tuning job.

Advantages

  • Ensures safe operation through mandatory confirmation tiers for destructive actions.
  • Provides clear, standardized procedures for interacting with the Agent Platform SDK.
  • Reduces errors by requiring explicit project and region information.
  • Maintains auditability through required usage attribution for all API interactions.

Usage examples

“List my currently running tuning jobs in project my-project-123, region us-central1.”
The agent will execute a Python script using the GenAiTuningServiceClient to list jobs filtered by running or pending states and display their names and base models.
“Cancel the tuning job with ID 1234567890123456789 in us-central1.”
The agent will present a dry-run confirmation card detailing the job, its effect, and ask for explicit user confirmation before proceeding in the next turn.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/agent-platform-tuning-management/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip agent-platform-tuning-management.zip -d .agents/skills

Documentation by Google

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.

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-tuning-management)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:agent-platform-tuning-management)

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:

    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:

    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 because of an invalid filter (e.g., 400 Provided filter is not valid), fix or drop the filter parameter and re-run. If it fails with an error indicating a missing or inaccessible project or job ID (403 Permission Denied, 404 Not Found, or invalid resource ID), you MUST inform the user that the project or tuning job does not exist or cannot be accessed, prompt the user to provide a valid Project ID or Job ID, and stop tool execution immediately to wait for their response (do NOT loop or assume the resource is valid).

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, always execute Python code via run_command (do NOT use curl). If the user gave a project ID or number, use that exact value (otherwise fall back to the configured project). Pass page_size=20 and read the first page (only page through every job if the user explicitly asks for a total count or the full list). Filter server-side when asked for running jobs (state="JOB_STATE_RUNNING" OR state="JOB_STATE_PENDING") or a specific model display name (display_name:"NAME"), print job.tuned_model_display_name, and note when first_page.next_page_token is set so the 20 most recently updated jobs are not presented as the entire history:

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"  # If the user gave a project ID/number, use it explicitly
region = "YOUR_REGION"
parent = f"projects/{project_id}/locations/{region}"

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

request = {
    "parent": parent,
    "page_size": 20,
    # Optional server-side filter, e.g.:
    # "filter": 'state="JOB_STATE_RUNNING" OR state="JOB_STATE_PENDING"',
}
response = client.list_tuning_jobs(request=request)
first_page = next(iter(response.pages), None)
for job in (first_page.tuning_jobs if first_page else []):
    print(f"Name: {job.name}")
    print(f"Display Name: {job.tuned_model_display_name}")
    print(f"Base Model: {job.base_model}")
    print(f"State: {job.state.name}")
if first_page and first_page.next_page_token:
    print("Note: More jobs exist beyond this first page of 20 most recently updated jobs.")
2. Getting Details for a Specific Job (Tier R)

If the user provides a Tuning Job ID and asks for its status, always execute Python code via run_command (never use curl). If the user gave a project ID or number, use it explicitly:

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"  # If the user gave a project ID/number, use it explicitly
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.name}")
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 tuning job:

Safety Check: Follow the Tier D (Destructive / Irreversible) confirmation rules in the Operation Tiers section above:

  • Never report an outcome you did not observe: Never claim a job was canceled or report a backend error without actually observing it from an API call.

  • Present the confirmation preview with Expected Effect conditioned on the job's observed state (for active PENDING/RUNNING jobs: stops in-progress training, which cannot be resumed, without deleting the base model or dataset; if the job is already in a terminal state SUCCEEDED, FAILED, or CANCELLED, state its current terminal state explicitly and note that no active training is running).

  • Wait for explicit user confirmation in a new turn before calling client.cancel_tuning_job(name=name) via Python (run_command), and report the actual API response verbatim.

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"  # Use the EXACT project ID/number the user gave
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}")

Files

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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

Security

What our scanner noticed· Low risk

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/agent-platform-tuning-management · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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