/agent-platform-tuning

name: agent-platform-tuning
metadata:
  version: "1.0.2"
  category: AiAndMachineLearning
description: >-
  Agent Platform Model Tuning. Use when you need to fine-tune open models
  or Gemini models using Agent Platform infrastructure. Don't use for model
  training outside Agent Platform, model deployment to endpoints (use
  `agent-platform-deploy`), or managing serving endpoints (use
  `agent-platform-endpoint-management`).

Agent Platform Model Tuning

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

Overview

This skill provides procedural knowledge for fine-tuning Large Language Models (both Open Models and Gemini Models) using Agent Platform's tuning service. It covers the entire lifecycle from environment setup and data preparation to job configuration, monitoring, and deployment.

Workflow Decision Tree

1. **Project & Region Verification Check**: Has the user provided the Google Cloud project and region?

• **No** → **STOP tool execution immediately**. Do NOT run verification

commands (`gcloud services list`, `gcloud projects get-iam-policy`), do NOT create resources, and do NOT begin dataset preparation. Prompt the user to specify or confirm the project and region (e.g. "Could you please specify which Google Cloud project and region you would like to use?").

• If the user's inquiry is solely to check or verify environment

readiness (APIs, IAM, service agents), ask ONLY for the project and region. Do NOT ask for the model category.

• If the user is requesting a tuning workflow and also omitted whether

they want to tune an Open Model or a Gemini Model, you may ask both questions together.

• **Yes** → Proceed.

2. **Model Category Identification**: Has the user explicitly stated whether they want to tune an **Open Model** or a **Gemini Model**?

• **No** →
• **EXCEPTION for Environment Verification Inquiries:** If the user is

only asking to check or verify that the environment, APIs, IAM permissions, or service agents are ready for tuning, do NOT ask for the model category. Verify the environment once the project and region are known and confirm readiness.

• Otherwise, **STOP tool execution**. Ask the user if they want to

tune an Open Model or a Gemini Model. **General Setup and Prerequisite Inquiries (e.g., "What environment setup is needed?"):** If the user asks what environment setup, prerequisites, APIs, or permissions are needed to start fine-tuning, and has not yet chosen a model category:

• Describe the setup requirements (APIs, IAM permissions/service

agents, and Python SDKs).

• Regarding Cloud Storage: state that an existing Cloud Storage bucket

is needed for datasets and artifacts (e.g., `gs://<existing-bucket>`). **CRITICAL:** Do NOT instruct the user to create a bucket, do NOT output a `gcloud storage buckets create` command in setup instructions, and do NOT assume a non-existent bucket exists (users may not have bucket creation permissions and will provide their own existing bucket).

• You MUST explicitly conclude your prose response with the direct

question: **"Would you like to tune an Open Model or a Gemini Model?"** (do NOT bury this choice inside a bulleted "Suggested Next Steps" list). Never provide setup instructions without explicitly asking this question in prose. (Note: if they ask to actively check or verify a project whose ID or region is missing, ask for the project and region first without running tool calls).

• If the user provides a specific tuning purpose, you should recommend

three models: one Open Model, one Gemini Model, and a third generally recommended choice. Briefly list the pros and cons of each (e.g., Gemini models might be more expensive, etc.). **CRITICAL:** You must read `references/models.md` during this step and only recommend models explicitly listed in that catalog. Never recommend uncataloged or unsupported models like `google/gemma-2-9b-it`, `gemma-2`, or `Mistral` — only recommend supported models such as Gemma 3 (`google/gemma3@gemma-3-12b-it`), Qwen 3 (`qwen/qwen3@qwen3-8b`), or Llama 3.1 (`meta/llama3_1@llama-3.1-8b`). For Gemini models, ONLY recommend `gemini-2.5-flash` (recommended for general/coding/chat) or `gemini-2.5-pro`. Never recommend `gemini-1.5-flash-002`, `gemini-1.5-pro-002`, or `gemini-1.5-flash`, which are deprecated and unsupported by the tuning service. If the user names a model that is not in the catalog, follow the fallback rule in that catalog. Do not proceed with model configuration until the category is confirmed.

• **Distillation requests**: If the user asks to distill a model, names a

teacher model, or wants a smaller model to learn from a larger one, this is an Open Model workflow using distillation. Read "Distillation" in `references/models.md` and use only the teacher and student models listed there.

• **Yes** → Proceed.

3. **Environment Check**: Has the environment (Auth, APIs, IAM,