Browse skills

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.

• Moving a Gemini application to Google Cloud
• Using Google Cloud credits for Gemini API calls
• Unifying agent authentication and billing

Manage BigQuery datasets, tables, views, and jobs, and run SQL queries for basic data analysis. Provides setup examples and references for data ingestion, command-line operations, client libraries, access permissions, and infrastructure configuration.

• Create BigQuery datasets and tables
• Search and aggregate data with SQL
• Load data and manage processing jobs

Prepare a local development environment for the Data Manager API with authentication and library installation guidance. It covers five programming languages and companion utilities for formatting, hashing, and encrypting user identifiers before ingestion.

• Preparing a local environment for the Data Manager API
• Installing client and identifier utility libraries
• Configuring local credentials and access scopes

Set up the official Google Ads MCP Server, a connector that lets AI assistants retrieve campaign data and reporting metrics through natural language. The guide covers installation, credentials, client configuration, and connection checks.

• Connecting Claude Code or Cursor to Google Ads
• Configuring API credentials and environment variables
• Testing campaign queries in natural language

Configure Cloud Logging within one Google Cloud project. Set storage locations, retention periods, routing rules, and viewing permissions; create metrics from logs; and adjust stored volume through exclusions or sampling, which retains only a portion of entries.

• Set regional log storage and retention periods
• Restrict access to sensitive logs
• Count specific errors with logs-based metrics

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.

• Send a test prompt to Gemini
• Call Llama or DeepSeek with the OpenAI SDK
• Write code to call an existing custom endpoint

Investigate 403 and 500 errors in Google Cloud Gemini Enterprise Agent Platform. Examine logs, registered destinations, agent identities, and authorization policies to produce a diagnostic report with evidence, recommended fixes, and verification steps.

• Investigating 403 errors when an agent calls an external API
• Diagnosing Agent Runtime 500 errors involving Model Armor
• Checking registration and permissions for failing MCP connections

Set up credentials and a development environment for your first Google Ads API request to retrieve campaigns. Choose from six official client libraries or direct HTTP requests, with guidance for account IDs, developer tokens, and common authorization errors.

• Prepare credentials for a first Google Ads API integration
• Build an example that retrieves advertising campaigns
• Choose a client library or direct REST requests

Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.

• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.

• Collect logs from multiple projects in one central bucket
• Query distributed logs without copying their data
• Set log access boundaries by project or log type

Deploy and manage applications on Cloud Run using source code or container images. Choose services for web requests, jobs for tasks that finish, or worker pools for continuous background processing, with guidance on execution settings and deployment failures.

• Deploy a web application from source code or a container
• Create and run batch jobs with parallel tasks
• Deploy continuous queue-processing workers

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.

• Build evaluation datasets from conversation logs
• Generate simulated user scenarios
• Choose scoring criteria for responses and tool use

Learn Cloud Build fundamentals and set up a basic build trigger for source code changes. Covers project preparation, configuration files, manual trigger runs, and checking build history and logs in Google Cloud.

• Learn build steps, artifacts, and configuration file basics
• Create a trigger that builds code after a Git push
• Run an existing trigger manually

Inspect and manage model tuning jobs, which adapt models through additional training, in Agent Platform. List jobs, check their status and details, and cancel running jobs after reviewing the target and explicitly confirming the action.

• List running model tuning jobs
• Check the status and details of a specific job
• Cancel a long-running job after confirmation

Manage saved prompts in Agent Platform with Python, including creation, listing, retrieval, version tracking, and deletion. Retrieve a specific version and fill template variables to reuse a prompt with its associated model.

• Create a managed prompt template
• List saved prompts and inspect their contents
• Retrieve a specific version and fill template variables

Prepare to fine-tune open models or Gemini models on Agent Platform. Check the environment, convert and validate training data, choose supported models and settings, and estimate tuning costs for open models.

• Checking whether an Agent Platform tuning environment is ready
• Converting and validating training datasets
• Choosing models and training settings for a task

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.

• Search for skills that provide a needed capability
• Upload or update skills in the registry
• Inspect skill details and revision histories

Write SQL that uses BigQuery’s built-in machine learning and generative AI for forecasting, anomaly detection, identifying drivers of change, searching by meaning, and classifying or summarizing text stored in your data.

• Forecasting future values from time-series data
• Detecting anomalies and investigating drivers of change
• Searching documents by meaning

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.

• Configure alerts for agent latency and error rates
• Monitor rapid increases in token usage
• Monitor safety triggers and permission denials

Automate Google Analytics administration through its Admin API. Manage accounts, properties, data streams, custom dimensions for additional analysis fields, event settings, and product links, with guidance on authentication and official client libraries.

• List accessible accounts and properties
• Create properties and configure data retention
• Manage custom dimensions and key events

Explore document collections in Agent Platform RAG Engine, list their files, and retrieve relevant passages with source references. Use retrieved material to generate answers grounded in the collection through Python SDKs.

• Listing available document collections and files
• Inspecting a specific RAG corpus
• Finding relevant passages and their sources

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.

• Listing endpoints and checking their configuration
• Creating an endpoint before model deployment
• Updating endpoint display names or labels

Turn existing PromQL or ListTimeSeries queries into Cloud Monitoring chart definitions in textproto, a text format for Protocol Buffers. Generate readable titles, axis labels, units, and plot types, then validate the resulting widget files.

• Create chart definitions from existing monitoring queries
• Set chart titles, axis labels, and units
• Prepare widget definitions for dashboard provisioning