Agent Skills for Google products and technologies
Install
npx skills add google/skillsgh skill install google/skillsgit clone --depth 1 https://github.com/google/skillsSkills in this collection
- Agent Platform Alert ConfigurationGoogleOfficialConfigures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics.
- Agent Platform DeployGoogleOfficialDeploy 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.
- Agent Platform Endpoint ManagementGoogleOfficialManages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
- Agent Platform Eval FlywheelGoogleOfficialMeasures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including…
- Agent Platform InferenceGoogleOfficialConnects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy…
- Agent Platform Migrate From Ai StudioGoogleOfficialGuides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).
- Agent Platform Model RegistryGoogleOfficialAgent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
- Agent Platform Prompt ManagementGoogleOfficialManages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
- Agent Platform Rag Engine ManagementGoogleOfficialManage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
- Agent Platform Skill RegistryGoogleOfficialInteract 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.
- Agent Platform TroubleshootingGoogleOfficialTroubleshoots Google Cloud Gemini Enterprise Agent Platform issues (Agent Gateway, Registry, Identity, Policies, Model Armor, Identity-Aware Proxy (IAP)). Use when agent requests fail with 403 (especially unauthorized egress), Agent Runtime queries return 500, or gateway/IAP logs show permission errors.
- Agent Platform TuningGoogleOfficialAgent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
- Agent Platform Tuning ManagementGoogleOfficialManages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
- Alloydb BasicsGoogleOfficialManages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
- API Gateway Backend Security And Rate LimitingGoogleOfficialSecures and rate limits backend services behind Google Cloud API Gateway across OpenAPI 2.0 and OpenAPI 3.0.x API configurations, including MCP-enabled gateways. Use when configuring API key or JWT authentication, locking down Cloud Run or Cloud Run functions backends with a gateway service account, reading authenticated claims from X-Apigateway-Api-Userinfo, setting per-project request quotas…
- API Gateway MCP ServerGoogleOfficialConfigures Google Cloud API Gateway to act as a remote Model Context Protocol (MCP) server (Public Preview) that exposes REST APIs as agent tools over HTTP (POST /mcp). Use when migrating OpenAPI 2.0 specs to OpenAPI 3.x for MCP, annotating specs with x-google-api-management.mcp and x-google-mcp-tool, securing tools/list with JWTs or API keys, connecting Google ADK (McpToolset) or Apigee API hub…
- Apigee Agent Gateway GovernanceGoogleOfficialStands up an Apigee API proxy that a Google Cloud Agent Gateway calls as an ext-proc (Service Extension) callout on an agent's LLM traffic, to meter token usage per model, extract caller identity, and apply Model Armor prompt screening or a semantic cache.
- Application Design Center Design DeployGoogleOfficialProcesses GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures.
- Assuredworkloads Environment AssessorGoogleOfficialDiscover Google Cloud resource telemetry and effective Org Policies, evaluate brownfield workloads against sovereign data boundary and compliance standards (EU Data Boundary with Support, FedRAMP Moderate/High, DoD IL4/IL5, ITAR, CJIS, US/EU Regional Data Boundaries), and recommend the optimal Assured Workloads Framework.
- Assuredworkloads Frameworks AuthorGoogleOfficialAuthors custom cloud controls and customized frameworks for Assured Workloads when built-in resources cannot satisfy core requirements. Use when creating or synthesizing custom data boundary resources. Don't use for browsing or mapping existing built-in frameworks (use assuredworkloads-frameworks-navigator instead).
- Assuredworkloads Frameworks NavigatorGoogleOfficialActs as a Data Boundary Compliance Analyst that maps user compliance queries against Data Boundary Frameworks and cloud controls, validates matched resources, and recommends appropriate Frameworks or cloud controls.
- Bigquery Ai MlGoogleOfficialLeverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic…
- Bigquery BasicsGoogleOfficialManages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.
- Bigquery BigframesGoogleOfficialGenerates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
- Bigquery ObservabilityGoogleOfficialProvides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot…
- Bigquery OptimizationGoogleOfficialProvides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs.
- Bigquery Slot Cost OptimizerGoogleOfficialAnalyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues.
- Bigquery TroubleshootingGoogleOfficialProvides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads.
- Bigquery UdfsGoogleOfficialCreates, deploys, and troubleshoots BigQuery User-Defined Functions (UDFs) and Remote Functions across SQL, JavaScript, Python, and Cloud Run endpoints. Use when writing custom scalar or aggregate functions in BigQuery, installing PyPI packages or Cloud Storage libraries in a Python or JavaScript UDF, or calling Google Cloud and external APIs from a Python UDF (WITH CONNECTION) or Remote…
- Bigtable BasicsGoogleOfficialAssists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs.
- Cloud Build BasicsGoogleOfficialTeaches the fundamentals of Cloud Build on Google Cloud. Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.
- Cloud Databases OnboardingGoogleOfficialGuides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud.
- Cloud Logging Configuration BasicsGoogleOfficialConfigure single-project Cloud Logging on Google Cloud: regional log buckets, log sinks, log views, restricting or hiding sensitive logs in the default view (_Default) filter, IAM permissions for views (Logs View Accessor, IAM conditions), logs-based metrics, log exclusions, and sampling. Don't use for cross-project logging or multi-project setups.
- Cloud Logging Cross Project ConfigurationGoogleOfficialConfigure and troubleshoot Google Cloud cross-project centralized logging and read-time aggregation. Use when: - Setting up log routing from multiple projects/folders/organizations to a central log bucket. - Creating cross-project log sinks and configuring central log buckets. - Troubleshooting cross-project routing. Don't use for single-project basic configurations.
- Cloud Logging Query GenerationGoogleOfficialGenerates Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.
- Cloud Monitoring Chart GenerationGoogleOfficialGenerates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions.
- Cloud Monitoring List Time Series RequestGoogleOfficialGenerates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.
- Cloud Monitoring Metric SelectionGoogleOfficialRetrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.
- Cloud Monitoring Notification Channels ConfigurationGoogleOfficialGenerates Terraform HCL configurations for Cloud Monitoring notification channels (`google_monitoring_notification_channel`) and adds notification channels to alerting policies (`google_monitoring_alert_policy`) in Google Cloud projects.
- Cloud Monitoring Promql QueryGoogleOfficialGenerates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.
- Cloud Network InsightsGoogleOfficialGuides and automates setup, onboarding, and synthetic monitoring operations for Cloud Network Insights in Google Cloud and Broadcom AppNeta. Use when enabling Cloud Network Insights, deploying or troubleshooting Monitoring Points across cloud and on-premises environments, configuring Network or Web monitoring policies and AppNeta alarm rules, configuring alert policies, querying path alarms and…
- Cloud Run Alert ConfigurationGoogleOfficialConfigures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization…
- Cloud Run BasicsGoogleOfficialManages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
- Cloud SQL BasicsGoogleOfficialThis file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL.
- Cloud Trace QueryingGoogleOfficialQuery Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services. Use when investigating slow requests, analyzing trace hierarchies, or resolving latency regressions. Do NOT use for querying non-GCP telemetry or database query optimization outside Cloud Trace.
- Data Manager API Audience IngestionGoogleOfficialGuides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data…
- Data Manager API Event IngestionGoogleOfficialGuides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data…
- Data Manager API SetupGoogleOfficialGuides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the API.