google/agent-platform-endpoint-management
>- 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.
npx skills add https://github.com/google/skills --skill agent-platform-endpoint-management
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.
Before executing any commands on behalf of the user, you MUST adhere to the
following safety tiers based on the action requested:
list, describe, get)create, update)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.
presenting the confirmation prompt. Stop and wait for the user's reply;
only execute after explicit 'Yes' / approval.
delete)delete it"). Ask for confirmation IMMEDIATELY — before any pre-flight
checks (don't describe first, don't check if the endpoint is empty
first).
typed confirmation. Wait for the user to reply in a new turn.
CRITICAL: Before running any commands, you MUST ensure the environment is
correctly initialized by following these steps:
credentials and configure active Application Default Credentials (ADC) for
Agent Platform access:
gcloud auth login
gcloud auth application-default login
gcloud config set project $PROJECT_ID
--region=$LOCATION_ID on each command below. DoNOT use global. Ask the user to specify the region if not provided.
Use this command to discover existing endpoints in a specific region and
retrieve their IDs. No confirmation is required.
gcloud ai endpoints list \
--region=$LOCATION_ID
*(Optional)* For pagination, you MUST use --limit=$LIMIT to restrict the total
number of returned endpoints. You can also append --page-size=$PAGE_SIZE to
control API chunking, or --page-token=$PAGE_TOKEN for next pages.
> [!IMPORTANT]
>
> Always specify the --region. Do NOT use 'global'. Ask the user to specify if
> not provided.
Retrieve the full metadata for a specific endpoint. No confirmation is required.
gcloud ai endpoints describe $ENDPOINT_ID \
--region=$LOCATION_ID
Create a new endpoint resource. The parent resource is the location. **Action
requires an inline confirmation card before proceeding.**
gcloud ai endpoints create \
--region=$LOCATION_ID \
--display-name="my-endpoint"
> [!IMPORTANT]
>
> You MUST seek interactive confirmation first. Your confirmation prompt
> MUST show the literal command string. For example:
>
> `bash
> gcloud ai endpoints create --region=$LOCATION_ID --display-name="my-endpoint"
> `
>
> Or the exact flags. Do not execute this command in the same turn as proposing
> the confirmation.
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:
>
> `bash
> 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.
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.
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.
aiplatform.admin or owner role isassigned.
undeployed.
Take google/agent-platform-endpoint-management from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.