Find and manage agent skills in the Gemini Enterprise Agent Platform Skill Registry, a catalog of available capabilities. The skill supports searching, uploading, updating, deleting, inspecting revision histories, checking operation status, and creating local starter files.
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Interact with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills. Use this skill to enable agents to register functionality or discover new capabilities.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill provides instructions for interacting with the Skill Registry on the Gemini Enterprise Agent Platform.
Mandatory Validation: ALWAYS execute the environment validation check before performing any operations.
Before any operation, you must validate the core environment.
# Execute the validation script
python3 scripts/validate_env.py
Ensure you have the latest Google Cloud credentials and libraries installed.
# Install required libraries
pip install google-auth requests
# Authenticate with Google Cloud
gcloud auth application-default login
The following variables are required for operations:
GCP_PROJECT_ID: Your Google Cloud Project ID.GCP_LOCATION: The region (e.g., us-central1).Quickly search for available skills in the registry:
python3 scripts/skill_registry_ops.py search \
--query "test skill" \
--top-k 5
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로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
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Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
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Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
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Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.
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Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.
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Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.
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Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.