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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

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

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

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.

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

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

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

Use Google's APIs to review search performance, page speed, indexing status, and organic traffic. Combine real visitor measurements with search and analytics data to identify SEO issues and create reports.

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• Reviewing search clicks, impressions, and rankings
• Tracking page experience metrics over 25 weeks
• Checking whether Google has indexed specific URLs

Create or update Claude skills that capture specialized knowledge and repeatable workflows. Guides the organization of instructions, scripts, references, and templates, then covers initialization, validation, and packaging into a distributable skill file.

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• Turning recurring work into a reusable Claude skill
• Organizing internal knowledge and API instructions
• Revising an existing skill after practical use

Plan and operate Google Cloud Platform infrastructure using services such as Cloud Run and GKE, Google's managed Kubernetes service. Get guidance on deployment, access permissions, networking, monitoring, and cost controls for the system's requirements.

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• Planning an API with Cloud Run and Cloud SQL
• Reviewing GKE access permissions
• Designing event processing with Pub/Sub

Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.

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• Set up Wren and connect a database
• Load HubSpot or Stripe data through dlt
• Generate model definitions and add business metrics

Turn specialized knowledge and repeatable workflows into Codex skills. Follow guidance for defining usage examples, writing SKILL.md, organizing scripts and reference materials, validating the package, and improving existing skills through practical use.

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• Create a Codex skill for a recurring workflow
• Package company knowledge as reference materials
• Reorganize an existing skill and its supporting files

Create or update Codex skills that package specialized knowledge, repeatable workflows, and tool instructions. The guide covers concrete use cases, folder structure, reusable resources, starter generation, and basic validation of names and required metadata.

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• Turning recurring work into a reusable skill
• Packaging company knowledge and API instructions
• Reorganizing an existing skill and its reference files

Turn recurring tasks and specialized knowledge into reusable agent skills. Plan instructions and supporting files, generate a starting structure, refine existing skills, and validate and package the result as a distributable .skill file.

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• Turn a recurring workflow into an agent skill
• Organize reference documents and reusable templates
• Refine instructions in an existing skill

Sign in to a cloud provider’s command-line tool using a verification URL and one-time code approved on the user’s device. Check installation and account identity, save the login, and run commands within that user’s permissions.

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• Sign in to a cloud CLI from a separate device
• Check whether required CLIs and helpers are installed
• Verify the cloud account before running commands

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

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• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Helps adapt machine learning training to multiple GPUs and machines with Ray Train. Covers resource allocation, recovery from saved training states, and Ray Tune searches that compare settings such as learning rate and batch size.

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• Adapt existing PyTorch training to multiple GPUs
• Run HuggingFace training across multiple machines
• Compare learning rates and batch sizes with Ray Tune

Build infrastructure as code with Terraform across AWS, Azure, and GCP. Create reusable modules, manage state records that track resources, configure environments and providers, and validate proposed changes before applying an explicitly approved plan.

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• Creating reusable Terraform modules
• Migrating state storage or importing existing resources
• Managing settings across development and production

Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.

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• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Design cloud architectures and migration plans for AWS, Azure, and GCP. Produce service diagrams, selection rationale, security designs, cost estimates, and recovery strategies, with deployment and rollback plans that account for availability and operational monitoring.

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• Design a cloud architecture and explain service choices
• Plan a cloud migration and cutover checks
• Review cloud spending and resource sizes

Work with Apache Beam I/O connectors, components that read and write external data. Get guidance on connector usage, unit and integration tests, troubleshooting data connections, and the basic structure of new connectors.

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• Add BigQuery or Kafka reads and writes to a pipeline
• Run unit and integration tests for connectors
• Investigate data source or destination issues

Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.

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• Test a Beam pipeline locally
• Configure pipeline execution on Dataflow
• Review Flink or Spark execution settings

Helps investigate and modify Apache Beam's automated build and test workflows in GitHub Actions. Explains workflow types, failure logs, targeted reruns, local reproduction, and the cloud credentials needed for workflows that access GCP.

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• Investigating failed checks on a pull request
• Rerunning a specific test workflow
• Reproducing CI test commands locally

Guides Java SDK development in Apache Beam, from finding relevant modules to building code, running focused tests, and trying examples. Also explains how to publish modified modules locally and build SDK containers.

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• Finding the Java SDK module to modify
• Running a specific test class or method
• Checking setup with the WordCount example