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/agent-platform-deploy

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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for running model evaluations (use `agent-platform-eval-flywheel` skill).

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Use this Skill: https://skilld.dev/gh/google/skills/agent-platform-deploy

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referencestroubleshooting.md

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Troubleshooting

Deployment Failure: Quota or Resource Exhausted

If your deployment fails (or stays in an error state) due to QUOTA_EXCEEDED or RESOURCE_EXHAUSTED errors, the specific hardware requested (e.g., NVIDIA_L4 or g2-standard-24) is either not available in your chosen region or exceeds your project's quota limits.

Solution: Look closely at the error message returned. It will often recommend an alternative region or machine type that currently has availability. Ask the user for confirmation to retry the deployment using the suggested --region or --machine-type parameters.

[!WARNING] If the alternative suggestions involve changing the machine type or accelerator, you MUST recalculate the estimated cost by re-running scripts/calculate_cost.py with the new params (see the cost-estimation step in SKILL.md §3), warn the user about list prices versus actual billing, and get their explicit confirmation for the new cost before retrying the deployment.

Source: SKILL.md on GitHub

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This skill facilitates model deployment on Google Cloud's Agent Platform with built-in safety confirmations for mutating tasks. There are security considerations related to potential command injection in helper scripts and the use of administrative IAM commands, which should be reviewed to ensure proper sanitization and least-privilege access.
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Signed by skilld at 99c871e. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

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Capability

metadata: { "version": "1.0.3", "category": "AiAndMachineLearning" }

/agent-platform-deploy