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Official agent skill

Agent Platform Tuning Management

by google in google/skills

Manages GenAI tuning jobs in Agent Platform. An agent skill from google/skills.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Agent Platform Tuning Management

skills CLI
$ npx skills add google/skills --skill agent-platform-tuning-management -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills agent-platform-tuning-management --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/agent-platform-tuning-management .claude/skills/agent-platform-tuning-management && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-platform-tuning-management
GitHub stars
21k
Token cost
~1.9k tokens
SKILL.md length
798 words
Files
1
Skills in repo
145
Repo updated
First seen
Licence
Apache-2.0

At a glance

Manages GenAI tuning jobs in Agent Platform. An agent skill from google/skills.

  • Fine-tuning models (use agent-platform-tuning)
  • SKILL.md covers Safety & Confirmation Tiers…, Phase 0: Environment Setup, Workflow Decision Tree and Using the Python SDK
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Deploying models to endpoints (use agent-platform-deploy)

What it does

Agent Platform Tuning Management is an agent skill from google/skills, published by the product's own GitHub organization. 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).

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Fine-tuning. It works with Python. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

  • Fine-tuning models (use agent-platform-tuning)
  • Deploying models to endpoints (use agent-platform-deploy)
  • Managing serving endpoints (use agent-platform-endpoint-management)

Example prompts

  • “Use the agent-platform-tuning-management skill to manage GenAI tuning jobs in Agent Platform. An agent skill from google/skills”
  • “/agent-platform-tuning-management”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 8a1ac05. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Platform Tuning Management loads about 1.9k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 798 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google/skills at commit 8a1ac05, republished under its Apache-2.0 licence (© google). 798 words, ~1,859 tokens.

Download SKILL.mdSave it as .claude/skills/agent-platform-tuning-management/SKILL.md (or your agent's skills folder).
name
agent-platform-tuning-management
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`).
metadata.version
1.0.0
metadata.category
AiAndMachineLearning

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)
Show full SKILL.md (288 more words)Show less

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}")
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}")

© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/cloud/agent-platform-tuning-management of google/skills.

Open the folder on GitHubat commit 8a1ac05

Compare with similar skills

Agent Platform Tuning Management next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Platform Tuning Management compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Platform Tuning Management this skillgoogle/skills21k—~1.9kAutomated safety check: PassApache-2.0
Hugging Face Vision Trainerhuggingface/skills11k1 repos~7.5kAutomated safety check: PassApache-2.0
Axolotl Fine-Tuning ReferenceOrchestra-Research/AI-Research-SKILLs13k9 repos~1.2kAutomated safety check: PassMIT
SimPO Preference TrainingOrchestra-Research/AI-Research-SKILLs13k5 repos~1.5kAutomated safety check: PassMIT
Aqua CLIoracle/accelerated-data-science125—~2.1kAutomated safety check: PassUPL-1.0
Quaxnstarman/quax143—~5.5kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Agent Platform Tuning Management

What does Agent Platform Tuning Management do?

Manages GenAI tuning jobs in Agent Platform. An agent skill from google/skills. Agent Platform Tuning Management is an agent skill from google/skills, published by the product's own GitHub organization. Manages GenAI tuning jobs in Agent Platform.

When should I use Agent Platform Tuning Management?

Agent Platform Tuning Management fits situations like: fine-tuning models (use agent-platform-tuning); deploying models to endpoints (use agent-platform-deploy); managing serving endpoints (use agent-platform-endpoint-management).

How do I install Agent Platform Tuning Management in Claude Code?

Run `npx skills add google/skills --skill agent-platform-tuning-management -a claude-code`. Or copy the skill folder (skills/cloud/agent-platform-tuning-management in google/skills) into .claude/skills/agent-platform-tuning-management in your project. Claude Code loads it when a task matches its description.

How do I install Agent Platform Tuning Management in Codex?

Run `npx skills add google/skills --skill agent-platform-tuning-management -a codex`. Or copy the skill folder (skills/cloud/agent-platform-tuning-management in google/skills) into .agents/skills/agent-platform-tuning-management in your project. Codex loads it when a task matches its description.

Can I use Agent Platform Tuning Management in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill agent-platform-tuning-management -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-platform-tuning-management, .gemini/skills/agent-platform-tuning-management, .github/skills/agent-platform-tuning-management and .opencode/skills/agent-platform-tuning-management in your project.

What does Agent Platform Tuning Management need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Platform Tuning Management is instructions for the agent only. Our summary lists: Python 3.

Does Agent Platform Tuning Management access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Platform Tuning Management safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Platform Tuning Management use?

Agent Platform Tuning Management is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Platform Tuning Management use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Platform Tuning Management?

Skills that share tags, products or a category with Agent Platform Tuning Management: Hugging Face Vision Trainer (huggingface/skills, 11k stars), Axolotl Fine-Tuning Reference (Orchestra-Research/AI-Research-SKILLs, 13k stars), SimPO Preference Training (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Aqua CLI (oracle/accelerated-data-science, 125 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Platform Tuning Management?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 20,994 GitHub stars. The repository holds 145 skills in this directory. The repository was last updated on October 6, 2026.

Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.