Google

Add Google Mobile Ads banners to Android, iOS, or Unity apps. Guides implementation from defining the ad view and choosing its size to handling load events, loading the banner, and checking the completed integration.

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• Add a banner ad to an Android app
• Place an adaptive banner at the top or bottom of an iOS app
• Integrate a banner ad into a Unity app

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.

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

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.

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

Diagnose declines in Google Ads conversions, conversion value, and leads by comparing periods, traffic, device breakdowns, impression share, and account changes. Use live account data through the Google Ads MCP Server or receive queries and diagnostic guidance for manual execution.

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• Investigate sudden drops in conversions or conversion value
• Identify budget or ad rank limits on impression share
• Separate traffic declines from conversion rate declines

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.

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

Turn Cloud Monitoring metric definitions and resource filters into validated PromQL queries, a language for querying monitoring data. Choose suitable aggregation functions, preserve supplied resource names, and format queries for use in Cloud Monitoring.

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• Convert Cloud Monitoring metric definitions into PromQL
• Filter monitoring queries to specific resources
• Choose aggregations that match metric types

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.

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• 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.

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

Guides implementation of audience uploads, individual removals, and full replacements through the Data Manager API. It covers formatting and hashing identifiers, validating requests, and checking processing results for Customer Match and supported device ID audiences.

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• Upload Customer Match audience members
• Remove selected users from an audience
• Clear or replace an entire audience

Helps developers send conversion and event data to Google Ads, Google Analytics, and Floodlight through the Data Manager API. It covers destination setup, data formatting, validation, migration from other APIs, and checking processing results.

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• Implement offline conversion uploads to Google Ads
• Send recommended or custom GA4 events
• Migrate event ingestion from another Google API

Migrate an existing Android app from the legacy Google Mobile Ads SDK to GMA Next-Gen SDK. The skill maps dependencies, imports, classes, and methods, and guides changes to initialization, ad callbacks, and UI thread handling.

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• Replace legacy advertising SDK dependencies
• Update ad imports, classes, and method calls
• Adapt initialization and UI updates in callbacks

Integrate Google's IMA DAI SDK for dynamic ad insertion into web, mobile, and TV video players. The skill guides setup and stream requests for live or on-demand HLS and DASH content configured in Google Ad Manager.

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• Adding DAI live playback to a web video player
• Integrating on-demand video playback on Android or iOS
• Implementing DAI playback for tvOS, Cast, or Roku

Generate Python code to clean, transform, analyze, visualize, and model BigQuery data with BigQuery DataFrames. The skill keeps processing in the cloud and uses familiar table operations, with guidance for previews, schema checks, and machine learning.

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• Clean and transform BigQuery data with Python
• Preview data and check column types
• Visualize aggregated or sampled data

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.

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

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.

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

Create custom Google Analytics reports with user counts, page views, and breakdowns by city or date. The skill covers API setup, authentication, and checking whether selected measurements and grouping fields can be used together.

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• Report active users by city and date
• Automate recurring analytics reports
• Check which report fields work together

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.

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

Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.

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• Choosing a database for a new Google Cloud application
• Clarifying data models, workloads, and scale requirements
• Preparing and validating database provisioning code for review

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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• Choose a deployable model and supported hardware
• Estimate hourly deployment costs before approval
• Check deployment progress and test predictions

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.

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

Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

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• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age

Find relevant Google Cloud monitoring metrics for a service or resource. Retrieves current metric definitions through the API, filters them by the requested concepts, and presents selected metrics with their names, data types, units, and monitored resources.

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• Find CPU metrics for Compute Engine
• Identify Cloud Storage write throughput and request metrics
• Check metric data types and units

Build Cloud Monitoring requests from metric definitions and resource details. Generate JSON parameters, filters, and aggregation settings for retrieving time series data, then validate the request against the live API.

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• Build JSON requests to retrieve monitoring data
• Filter metrics for specific VMs or databases
• Set aggregation intervals and resource grouping

Turn plain-language requests into Cloud Logging queries for Google Cloud. Generate service-specific filters to find errors, investigate issues, or review audit logs using LQL, the language used to search these logs.

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• Finding recent error logs
• Filtering logs for a specific Google Cloud service
• Investigating who changed or accessed a resource