Agent Skillsgoogle/skills › agent-platform-endpoint-management

agent-platform-endpoint-management

GitHub

管理Agent Platform推理端点,涵盖创建、列出、描述、更新和删除操作。适用于模型部署前的端点准备及权限、配额故障排查。执行前需按安全等级进行确认,并严格遵循环境初始化流程。

skills/cloud/agent-platform-endpoint-management/SKILL.md google/skills

Trigger Scenarios

需要创建或管理Agent Platform端点 查询端点列表或详情 排查端点权限、配额或资源繁忙错误

Install

npx skills add google/skills --skill agent-platform-endpoint-management -g -y
More Options

Non-standard path

npx skills add https://github.com/google/skills/tree/main/skills/cloud/agent-platform-endpoint-management -g -y

Use without installing

npx skills use google/skills@agent-platform-endpoint-management

指定 Agent (Claude Code)

npx skills add google/skills --skill agent-platform-endpoint-management -a claude-code -g -y

安装 repo 全部 skill

npx skills add google/skills --all -g -y

预览 repo 内 skill

npx skills add google/skills --list

SKILL.md

Frontmatter
{
    "name": "agent-platform-endpoint-management",
    "metadata": {
        "category": "AiAndMachineLearning"
    },
    "description": "Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations."
}

Agent Platform Endpoint Management

Overview

This skill provides procedural knowledge for managing Agent Platform Endpoints. Endpoints are logical serving hosts that provide a stable URL for online predictions. You must create an endpoint before you can deploy a model to it.

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 (create, 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, --display-name="...") — natural-language paraphrases are NOT sufficient.
    • Same-turn restriction: NEVER execute the command in the same turn as presenting the confirmation prompt. Stop and wait for the user's reply; only execute after explicit 'Yes' / 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 describe first, don't check if the endpoint is empty 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.

Phase 0: Environment Setup

CRITICAL: Before running any commands, you MUST ensure the environment is correctly initialized by following these steps:

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

    gcloud auth login
    gcloud auth application-default login
    
  2. Set Project: Configure the active project for subsequent commands:

    gcloud config set project $PROJECT_ID
    
  3. Region: Always specify --region=$LOCATION_ID on each command below. Do NOT use global. Ask the user to specify the region if not provided.

1. Listing Endpoints (Tier R)

Use this command to discover existing endpoints in a specific region and retrieve their IDs. No confirmation is required.

gcloud ai endpoints list \
    --region=$LOCATION_ID

[!IMPORTANT] Always specify the --region. Do NOT use 'global'. Ask the user to specify if not provided.

2. Describing an Endpoint (Tier R)

Retrieve the full metadata for a specific endpoint. No confirmation is required.

gcloud ai endpoints describe $ENDPOINT_ID \
    --region=$LOCATION_ID

3. Creating an Endpoint (Tier M)

Create a new endpoint resource. The parent resource is the location. Action requires an inline confirmation card before proceeding.

gcloud ai endpoints create \
    --region=$LOCATION_ID \
    --display-name="my-endpoint"

[!IMPORTANT] You MUST seek interactive confirmation first. Your confirmation prompt MUST show the literal command string. For example:

gcloud ai endpoints create --region=$LOCATION_ID --display-name="my-endpoint"

Or the exact flags. Do not execute this command in the same turn as proposing the confirmation.

4. Updating an Endpoint (Tier M)

Update endpoint metadata such as display name or labels. Action requires an inline confirmation card before proceeding.

gcloud ai endpoints update $ENDPOINT_ID \
    --region=$LOCATION_ID \
    --display-name="new-display-name"

Check if the endpoint exists first by either listing or describing the endpoint.

[!IMPORTANT] You MUST seek interactive confirmation first. Your confirmation prompt MUST show the literal command string. For example:

gcloud ai endpoints update $ENDPOINT_ID --region=$LOCATION_ID --display-name="new-display-name"

Or the exact flags. CRITICAL: You are strictly prohibited from executing this command in the same turn as asking for confirmation. When you ask for confirmation, you MUST stop immediately and wait for the user to reply.

5. Deleting an Endpoint (Tier D)

Permanently delete an endpoint resource. Action requires explicit typed confirmation before proceeding.

gcloud ai endpoints delete $ENDPOINT_ID \
    --region=$LOCATION_ID

[!WARNING] All models must be undeployed from the endpoint before it can be deleted. Do not run describe until AFTER you have received typed confirmation to delete.

6. Traffic Splitting (Tier M)

You can manage traffic split between different models deployed on the same endpoint during an update. Action requires an inline confirmation card before proceeding.

# Example: Deploying a model with a specific traffic split is usually done
# via 'gcloud ai endpoints deploy-model'.

Refer to the agent-platform-deploy skill for instructions on deploying and undeploying models.

Troubleshooting

  • 403 Permission Denied: Ensure aiplatform.admin or owner role is assigned.
  • Quota Exceeded: Verify the region's endpoint quota in the Cloud Console.
  • Resource Busy: If a deletion fails, check if models are still being undeployed.

Version History

  • aabe37a Current 2026-07-05 15:28

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