{"generator":"This file is generated. Do not edit it by hand.","skills":[{"name":"agent-platform-alert-configuration","description":"Configures 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. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-alert-configuration/SKILL.md"},{"name":"agent-platform-deploy","description":"Deploy 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. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form \"is X deployed?\", \"list my endpoints\", or \"which regions have models running?\" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-deploy/SKILL.md"},{"name":"agent-platform-endpoint-management","description":"Manages 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-endpoint-management/SKILL.md"},{"name":"agent-platform-eval-flywheel","description":"Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when 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 dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-eval-flywheel/SKILL.md"},{"name":"agent-platform-inference","description":"Connects 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 SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-inference/SKILL.md"},{"name":"agent-platform-migrate-from-ai-studio","description":"Guides 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).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-migrate-from-ai-studio/SKILL.md"},{"name":"agent-platform-model-registry","description":"Agent 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-model-registry/SKILL.md"},{"name":"agent-platform-prompt-management","description":"Manages 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-prompt-management/SKILL.md"},{"name":"agent-platform-rag-engine-management","description":"Manage 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-rag-engine-management/SKILL.md"},{"name":"agent-platform-skill-registry","description":"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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-skill-registry/SKILL.md"},{"name":"agent-platform-troubleshooting","description":"Troubleshoots 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. Don't use for general Google Cloud Identity and Access Management (IAM) debugging or networking issues unrelated to the Agent Platform stack.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-troubleshooting/SKILL.md"},{"name":"agent-platform-tuning","description":"Agent 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`).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-tuning/SKILL.md"},{"name":"agent-platform-tuning-management","description":"Manages 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`).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/agent-platform-tuning-management/SKILL.md"},{"name":"alloydb-basics","description":"Manages 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/alloydb-basics/SKILL.md"},{"name":"application-design-center-design-deploy","description":"Processes 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. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/application-design-center-design-deploy/SKILL.md"},{"name":"bigquery-ai-ml","description":"Leverages 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 conditions, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/bigquery-ai-ml/SKILL.md"},{"name":"bigquery-basics","description":"Manages 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/bigquery-basics/SKILL.md"},{"name":"bigquery-bigframes","description":"Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/bigquery-bigframes/SKILL.md"},{"name":"bigtable-basics","description":"Assists 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. Don't use for generic Cloud SQL administration.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/bigtable-basics/SKILL.md"},{"name":"cloud-build-basics","description":"Teaches the fundamentals of Google Cloud Build (GCB). 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-build-basics/SKILL.md"},{"name":"cloud-databases-onboarding","description":"Guides 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. Don't use for general Google Cloud maintenance, managing existing databases, or database migrations.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-databases-onboarding/SKILL.md"},{"name":"cloud-logging-configuration-basics","description":"Configure single-project Google Cloud Logging: 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-logging-configuration-basics/SKILL.md"},{"name":"cloud-logging-cross-project-configuration","description":"Configure 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-logging-cross-project-configuration/SKILL.md"},{"name":"cloud-logging-query-generation","description":"Generates Logging Query Language (LQL) queries for Google Cloud Logging 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-logging-query-generation/SKILL.md"},{"name":"cloud-monitoring-chart-generation","description":"Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos 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. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-monitoring-chart-generation/SKILL.md"},{"name":"cloud-monitoring-list-time-series-request","description":"Generates 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-monitoring-list-time-series-request/SKILL.md"},{"name":"cloud-monitoring-metric-selection","description":"Retrieve, query, and identify relevant Google Cloud Monitoring metric descriptors for a GCP 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-monitoring-metric-selection/SKILL.md"},{"name":"cloud-monitoring-promql-query","description":"Generates 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.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-monitoring-promql-query/SKILL.md"},{"name":"cloud-run-basics","description":"Manages 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).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-run-basics/SKILL.md"},{"name":"cloud-sql-basics","description":"This 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. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/cloud-sql-basics/SKILL.md"},{"name":"data-manager-api-audience-ingestion","description":"Guides 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 Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/data-manager-api-audience-ingestion/SKILL.md"},{"name":"data-manager-api-event-ingestion","description":"Guides 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 Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/data-manager-api-event-ingestion/SKILL.md"},{"name":"data-manager-api-setup","description":"Guides 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. Don't use for implementing audience or event ingestion logic (use the data-manager-api-audience-ingestion or data-manager-api-event-ingestion skills instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/data-manager-api-setup/SKILL.md"},{"name":"datalineage-bigquery-asset-impact-analysis","description":"Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/datalineage-bigquery-asset-impact-analysis/SKILL.md"},{"name":"datalineage-summary","description":"Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/datalineage-summary/SKILL.md"},{"name":"detection-engineering-coverage-evaluation","description":"Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, generating new YARA-L 2.0 rules to close coverage gaps, and with user approval, deploy them to SecOps. Don't use when asked to perform threat hunting actions, and SOC investigative actions.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/detection-engineering-coverage-evaluation/SKILL.md"},{"name":"developer-device-platform-basics","description":"Provides guidance and instructions on managing remote devices on Developer Device Platform (DDP). Use when reserving remote Android devices, establishing connection tunnels, checking session status, or extending/cancelling leases. Don't use for iOS or local device/hardware inquiries.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/developer-device-platform-basics/SKILL.md"},{"name":"developing-genkit-dart","description":"Generates code and provides documentation for the Genkit Dart SDK. Use when the user asks to build AI agents in Dart, use Genkit flows, or integrate LLMs into Dart/Flutter applications.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/genkit-dart/SKILL.md"},{"name":"developing-genkit-go","description":"Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/genkit-go/SKILL.md"},{"name":"developing-genkit-js","description":"Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/genkit-js/SKILL.md"},{"name":"developing-genkit-python","description":"Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/genkit-python/SKILL.md"},{"name":"finding-google-skills","description":"Locates and loads the right Google product skill on demand from a remote catalog index, instead of preloading every skill. Use at the START of any request touching a Google product, API, or developer platform - including Google Cloud (GKE, Cloud Run, IAM, BigQuery, Vertex AI, Spanner), Google Ads, Google Analytics, Google Workspace (Gmail, Drive, Admin SDK), Chrome and Chrome extensions, Android, Firebase, YouTube, Google Maps, Gemini and the Gemini API, Google Play, and Flutter. Consult the index before answering from memory or searching the web. Don't use for non-Google products.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/developers/finding-google-skills/SKILL.md"},{"name":"firebase-basics","description":"Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation/switching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/firebase-basics/SKILL.md"},{"name":"gcloud","description":"Provides safety-critical validation, guardrails, and data reduction for gcloud CLI operations across Google Cloud Platform (GCP) services and infrastructure. Use when planning, generating, constructing, proposing, describing, or executing any gcloud CLI commands - including when answering questions about gcloud syntax, or formatting flags. Don't use when writing Google Cloud client library code or raw REST/gRPC API requests.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gcloud/SKILL.md"},{"name":"gemini-agents-api","description":"Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gemini-agents-api/SKILL.md"},{"name":"gemini-api","description":"Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gemini-api/SKILL.md"},{"name":"gemini-interactions-api","description":"Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gemini-interactions-api/SKILL.md"},{"name":"gemini-live-api","description":"Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — use the `gemini-api` skill for those.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gemini-live-api/SKILL.md"},{"name":"gke-ai-troubleshooting-handle-disruption-gpu-tpu","description":"Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-ai-troubleshooting-handle-disruption-gpu-tpu/SKILL.md"},{"name":"gke-ai-troubleshooting-jobset-interruption","description":"Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-ai-troubleshooting-jobset-interruption/SKILL.md"},{"name":"gke-ai-troubleshooting-tpu-dynamic-slices-monitoring","description":"Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-ai-troubleshooting-tpu-dynamic-slices-monitoring/SKILL.md"},{"name":"gke-ai-troubleshooting-tpu-metrics-monitoring","description":"Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU debugging.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-ai-troubleshooting-tpu-metrics-monitoring/SKILL.md"},{"name":"gke-ai-troubleshooting-tpu-vbar-oom","description":"Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle operations.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-ai-troubleshooting-tpu-vbar-oom/SKILL.md"},{"name":"gke-alert-configuration","description":"Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-alert-configuration/SKILL.md"},{"name":"gke-app-onboarding","description":"Manages GKE application onboarding, covering containerization, deployment manifests, and migration. Use when onboarding or deploying an application to GKE for the first time, or containerizing an app for GKE. Don't use for general GKE cluster administration or upgrades (use gke-basics or gke-upgrades instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-app-onboarding/SKILL.md"},{"name":"gke-backup-dr","description":"Configures Backup for GKE: the BackupRestore cluster addon, BackupPlan and RestorePlan resources, restore workflows, and CMEK-encrypted backups. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-backup-dr/SKILL.md"},{"name":"gke-basics","description":"Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-basics/SKILL.md"},{"name":"gke-batch-hpc","description":"Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-batch-hpc/SKILL.md"},{"name":"gke-cluster-autoscaler","description":"Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-cluster-autoscaler/SKILL.md"},{"name":"gke-cluster-creation","description":"Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-cluster-creation/SKILL.md"},{"name":"gke-compute-classes","description":"Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-compute-classes/SKILL.md"},{"name":"gke-cost-analysis","description":"Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-cost-analysis/SKILL.md"},{"name":"gke-cost-optimization","description":"Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-cost-optimization/SKILL.md"},{"name":"gke-custom-golden-image-discovery","description":"Discovers golden base images for creating GKE custom node images based on technical specifications or context clues. Use when finding the golden base image for custom GKE node creation, mapping cluster configuration parameters (GKE version, OS, architecture, accelerators, gVisor, cgroups) to image names, or querying GKE base image maps. Don't use for general GKE cluster creation (use gke-cluster-creation) or standard node pool management (use gke-basics).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-custom-golden-image-discovery/SKILL.md"},{"name":"gke-golden-path","description":"Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-golden-path/SKILL.md"},{"name":"gke-inference","description":"Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-inference/SKILL.md"},{"name":"gke-manifest-generation","description":"Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-manifest-generation/SKILL.md"},{"name":"gke-multitenancy","description":"Plans and configures multi-tenancy on GKE. Covers namespace isolation, RBAC planning for teams, resource quotas, LimitRanges, network isolation, and cost allocation. Use when designing GKE multi-tenancy, configuring GKE namespaces, setting up resource quotas, or isolating GKE teams. Don't use for single-tenant cluster configuration or general deployment instructions (use gke-basics or gke-app-onboarding instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-multitenancy/SKILL.md"},{"name":"gke-networking","description":"Plans, configures, and manages core GKE cluster networking. Covers private clusters, VPC-native configurations, DNS, node egress, Dataplane V2, and IP planning. Use when designing GKE networking layouts, configuring private clusters, setting up Dataplane V2, planning GKE IP ranges, or managing VPC- native cluster modes. Don't use for application ingress, load balancing, or service networking (use gke-service-networking instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-networking/SKILL.md"},{"name":"gke-observability","description":"Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-observability/SKILL.md"},{"name":"gke-platform-security","description":"Plans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ons, or managing GKE IAM roles. Don't use for workload-level security (Workload Identity, SecretProviderClass, PSS, NetPol, gVisor pod runtimeClassName; use gke-workload-security instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-platform-security/SKILL.md"},{"name":"gke-productionize","description":"Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-productionize/SKILL.md"},{"name":"gke-reliability","description":"Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-reliability/SKILL.md"},{"name":"gke-service-networking","description":"Configures GKE edge networking, traffic routing, load balancing, and private service endpoints. Use when configuring Gateway API manifests, standard Ingress, Cloud Armor WAF security policies, Container-Native Load Balancing (NEGs), Private Service Connect (PSC), or Google-managed SSL certificates on GKE. Don't use for core cluster IP planning, Dataplane V2 network policies, or node NAT egress (use gke-networking instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-service-networking/SKILL.md"},{"name":"gke-storage","description":"Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. Don't use for database administration or replication strategies outside volume provisioning context.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-storage/SKILL.md"},{"name":"gke-upgrades","description":"Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE upgrades, Kubernetes version bumps, node pool maintenance, GKE patching, cluster version management, release channel selection, maintenance windows, surge upgrades, stuck upgrades, or any GKE lifecycle management task — even casual mentions like \"we need to upgrade our clusters\" or \"plan our next GKE maintenance\" or \"our upgrade is stuck.\" Don't use for GKE cluster creation, application onboarding, general networking/routing setup, or security policy configurations (use gke-basics or relevant GKE skills instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-upgrades/SKILL.md"},{"name":"gke-workload-scaling","description":"Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-workload-scaling/SKILL.md"},{"name":"gke-workload-security","description":"Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (`audit_cluster.sh`), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling), and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing cluster security posture, isolating namespaces, applying pod security standards, setting up Workload Identity, or configuring network policies and secret volume mounts. Don't use for cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-workload-security/SKILL.md"},{"name":"gke-workload-troubleshooting","description":"Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/gke-workload-troubleshooting/SKILL.md"},{"name":"google-ads-api-account-diagnostics","description":"Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-ads-api-account-diagnostics/SKILL.md"},{"name":"google-ads-api-mcp-setup","description":"Guides developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve reporting metrics using natural language.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-ads-api-mcp-setup/SKILL.md"},{"name":"google-ads-api-quickstart","description":"Guides developers through Google Ads API quickstart: credential setup, choosing from 6 client libraries/REST, configuring environments, and running a \"retrieve campaigns\" script. Troubleshoots common setup errors: USER_PERMISSION_DENIED, login_customer_id issues, and DEVELOPER_TOKEN_NOT_APPROVED. Use this skill when: - The user asks how to get started with the Google Ads API. - The user needs to set up Google Ads credentials or developer tokens. - The user wants to write a quickstart/example script for Google Ads. - The user encounters errors like USER_PERMISSION_DENIED or DEVELOPER_TOKEN_NOT_APPROVED.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-ads-api-quickstart/SKILL.md"},{"name":"google-agents-cli-onboarding","description":"Onboarding entrypoint for agents-cli in Agent Platform. It should be used when the user wants to \"create a new agent\", \"develop an agent\", \"build an agent using ADK\", \"run the agent locally\", \"debug agent code\", \"test an agent\", \"evaluate an agent\", \"deploy an agent\", \"publish an agent\", \"monitor an agent\", or needs the ADK (Agent Development Kit) development lifecycle.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-agents-cli-onboarding/SKILL.md"},{"name":"google-analytics-admin-api-basics","description":"Manages Google Analytics account and property settings, enables the Analytics Admin API via the Cloud CLI, lists accounts and properties, and manages data streams, custom dimensions, conversion events, and integrations. Use when you need to programmatically configure Google Analytics accounts, provision properties, manage data retention, configure Measurement Protocol secrets, or manage Firebase and Google Ads links.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/analytics/google-analytics-admin-api-basics/SKILL.md"},{"name":"google-analytics-data-api-basics","description":"Manages Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized analytics reports, query metrics (like activeUsers, screenPageViews) and dimensions (like city, date), check metrics and dimensions compatibility, or verify API enablement. Don't use for Google Analytics Admin API operations (e.g., creating properties, managing users) or for front-end tracking installation.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/analytics/google-analytics-data-api-basics/SKILL.md"},{"name":"google-cloud-filestore-autoscale","description":"Inspects Google Cloud Filestore capacity and utilization, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds, or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-filestore-autoscale/SKILL.md"},{"name":"google-cloud-global-frontend-configuration","description":"Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-global-frontend-configuration/SKILL.md"},{"name":"google-cloud-networking-observability","description":"Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-networking-observability/SKILL.md"},{"name":"google-cloud-recipe-auth","description":"Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-recipe-auth/SKILL.md"},{"name":"google-cloud-recipe-foundation-builder","description":"Deploys a baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-recipe-onboarding or product-specific skills instead).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-recipe-foundation-builder/SKILL.md"},{"name":"google-cloud-recipe-onboarding","description":"Guides a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource. Use when a new developer wants to initialize their first Google Cloud project, configure billing, and verify deployment. Don't use for enterprise organization setup (use Google Cloud Setup guided flow for that instead). Don't use for complex multi-project architectures.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-recipe-onboarding/SKILL.md"},{"name":"google-cloud-scc-query","description":"Queries and retrieves active security findings, external exposures, toxic combinations, vulnerabilities, threats, and sensitive data risks from Google Cloud Security Command Center. Use when retrieving details for a security finding by its name, validating finding scope (e.g., verifying findingClass is TOXIC_COMBINATION, VULNERABILITY, EXTERNAL_EXPOSURE, or THREAT), or fetching finding details for triage. Don't use to draft remediations, apply patches, or execute configurations.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-scc-query/SKILL.md"},{"name":"google-cloud-slo-alert-configuration","description":"Configures PromQL-based Service Level Objective (SLO) alerting policies for Google Cloud resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting policies.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-slo-alert-configuration/SKILL.md"},{"name":"google-cloud-solution-agentic-ai-bidirectional-streaming","description":"Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-agentic-ai-bidirectional-streaming/SKILL.md"},{"name":"google-cloud-solution-agentic-ai-borderless-data-lakehouse","description":"Guides agents to discover requirements and design a governed, secure borderless open data lakehouse with agentic AI integration. Use when designing a multi-product architecture that connects data silos to AI agents, joining data across clouds, or running federated queries across Google Cloud and external data sources, including on-premises or other cloud providers. Don't use for simple single-cloud data warehouses or non-AI workloads.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse/SKILL.md"},{"name":"google-cloud-solution-agentic-ai-data-science-workflow","description":"Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-agentic-ai-data-science-workflow/SKILL.md"},{"name":"google-cloud-solution-agentic-analytics-spark-knowledge-catalog","description":"Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is accessed through federation mechanisms such as Apache Iceberg, other \"zero-copy ETL\" methods, or remote query push-down. Use this skill when designing an architecture for efficient analytics across large volumes of structured and unstructured data that's located in multiple systems and environments, including other cloud providers and on-premises.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-agentic-analytics-spark-knowledge-catalog/SKILL.md"},{"name":"google-cloud-solution-architecture","description":"Interactively discovers requirements for a specific cloud workload and generates design recommendations and architectural guidance to build a multi-product solution in Google Cloud. Use this skill to generate holistic, end-to-end design recommendations and architectural guidance for complex, multi-product workloads on Google Cloud for specific use cases. Don't use this skill when other specialized skills (e.g., product-specific or google-cloud-recipe-*) directly address the user's workload or use case.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-architecture/SKILL.md"},{"name":"google-cloud-solution-build-deploy-agents","description":"Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-build-deploy-agents/SKILL.md"},{"name":"google-cloud-solution-guided-gke-ai-migration","description":"Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use for brand new GKE inference deployments with no existing workload to migrate (use gke-inference instead). DO NOT use if the user intends to automate the migration via the Gemini Cloud Assist MCP server.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-guided-gke-ai-migration/SKILL.md"},{"name":"google-cloud-solution-hybrid-search-alloydb","description":"Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-hybrid-search-alloydb/SKILL.md"},{"name":"google-cloud-solution-multi-agent-security","description":"Designs, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-multi-agent-security/SKILL.md"},{"name":"google-cloud-solution-n-tier-serverless-web-app","description":"Assists in developing a secure n-tier serverless web application based on best practices and architecture guidance. Use when users need agentic assistance with designing and creating a multi-product solution in Google Cloud for a secure n-tier serverless web application. Don't use when designing VM or GKE-based architectures or when not using Google Cloud.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-n-tier-serverless-web-app/SKILL.md"},{"name":"google-cloud-solution-rag-enterprise-search-gke-sqldb","description":"Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-solution-rag-enterprise-search-gke-sqldb/SKILL.md"},{"name":"google-cloud-storage-basics","description":"Stores, retrieves, and manages data as objects in Cloud Storage (Google Cloud Storage, or GCS) buckets. Use when you need to interact with Cloud Storage — create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes and tiering (Standard, Nearline, Coldline, Archive), manage cost and lifecycle, protect data (versioning, encryption/CMEK, retention and Bucket Lock, object holds, soft delete), host static websites, trigger Pub/Sub notifications on object changes, mount buckets as a file system (gcsfuse), or optimize storage performance at any scale. Covers the gcloud storage / gsutil CLI, JSON and XML APIs, client libraries, Terraform, and Cloud Storage MCP servers. Don't use for block storage (Persistent Disk), data warehousing/analytics (BigQuery), or databases (Cloud SQL, Spanner, Bigtable, Firestore).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-storage-basics/SKILL.md"},{"name":"google-cloud-storage-fuse","description":"Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre).","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-storage-fuse/SKILL.md"},{"name":"google-cloud-waf-cost-optimization","description":"Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for build, deploy, and manage the workload cost-efficiently in Google Cloud.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-waf-cost-optimization/SKILL.md"},{"name":"google-cloud-waf-operational-excellence","description":"Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident management.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-waf-operational-excellence/SKILL.md"},{"name":"google-cloud-waf-performance-optimization","description":"Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-waf-performance-optimization/SKILL.md"},{"name":"google-cloud-waf-reliability","description":"Generates guidance for reliability, resilience, availability, redundancy, fault-tolerance, and disaster recovery (DR) for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use when the user asks to evaluate, design, or improve the reliability, resilience, availability, or disaster recovery capabilities of Google Cloud workloads.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-waf-reliability/SKILL.md"},{"name":"google-cloud-waf-security","description":"Generates security-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate workloads, identify security requirements, and provide actionable recommendations for IAM, network security, data protection, and operational security.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-waf-security/SKILL.md"},{"name":"google-cloud-waf-sustainability","description":"Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify environmental impact requirements, and provide actionable recommendations to build, deploy, and manage the workload sustainably in Google Cloud.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/google-cloud-waf-sustainability/SKILL.md"},{"name":"google-mobile-ads-android-migrate-to-next-gen","description":"Migrates Android applications from the old, legacy Google Mobile Ads (GMA) SDK (com.google.android.gms:play-services-ads) to the new GMA Next-Gen SDK (com.google.android.libraries.ads.mobile.sdk:ads-mobile-sdk). Provides comprehensive mapping tables for imports, classes, and method signatures to help determine migration steps. Use when migrating an existing Android codebase from the old, legacy GMA SDK to GMA Next-Gen SDK.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-mobile-ads-android-migrate-to-next-gen/SKILL.md"},{"name":"google-mobile-ads-banner","description":"Provides instructions to implement, integrate, or configure Google Mobile Ads (GMA) banner ads in Android, iOS, or Unity mobile applications. Use when the task involves setting up banner ads in a mobile application. Don't use for other ad formats like interstitial or rewarded ads.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-mobile-ads-banner/SKILL.md"},{"name":"google-mobile-ads-get-started","description":"Provides instructions for integrating the Google Mobile Ads (GMA) SDK. Use this skill when the user wants to get started with, install, integrate, set up, or configure the SDK for AdMob or Ad Manager, GMA Next-Gen SDK or mobile ads framework in an Android, iOS, or Unity application.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-mobile-ads-get-started/SKILL.md"},{"name":"google-mobile-ads-interstitial","description":"Provides instructions for implementing, integrating, or configuring Google Mobile Ads (GMA) SDK interstitial ads in Android, iOS, or Unity mobile applications. Use when the task involves setting up interstitial ads. Don't use for \"rewarded interstitial\" ads.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-mobile-ads-interstitial/SKILL.md"},{"name":"google-mobile-ads-rewarded","description":"Provides instructions for implementing, integrating, or configuring Google Mobile Ads (GMA) SDK rewarded ads in Android, iOS, or Unity mobile applications. Use when the task involves setting up rewarded ads. Don't use for \"rewarded interstitial\" ads.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/google-mobile-ads-rewarded/SKILL.md"},{"name":"iam-helper-for-policy-simulator","description":"Safely simulates and applies Google Cloud IAM v1 (Allow) policy changes. Uses the Policy Simulator to replay historical access logs against proposed policies to prevent breaking active workloads before applying the changes. Use when simulating or applying IAM v1 allow policies across Projects, Folders, or Organizations. Don't use for analyzing IAM v2 deny policies, VPC Service Controls, or performing general policy troubleshooting.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/iam-helper-for-policy-simulator/SKILL.md"},{"name":"iam-helper-for-privileged-access-management","description":"Manages the end-to-end lifecycle of on-demand, temporary access using Privileged Access Manager (PAM). Use when a user asks to create, read, update, or delete PAM entitlements, request temporary access, or approve/deny pending PAM grants. Do NOT use for permanent IAM policy bindings, troubleshooting IAM permission errors, or general Google Cloud resource provisioning.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/iam-helper-for-privileged-access-management/SKILL.md"},{"name":"ima-dai-sdk","description":"Integrates the Google Interactive Media Ads (IMA) Dynamic Ad Insertion (DAI) SDK into websites, web apps, mobile apps, or TV apps. Use when: - A video player needs to load and play HLS or DASH streams in web apps, Android apps, iOS apps, tvOS apps, Cast (CAF) receivers, or Roku channels. - The app needs to make use of a Google DAI livestream event asset key, or content source CMS ID, video ID for video on demand. Don't use this skill to load and play a VAST or VMAP URL.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/ima-dai-sdk/SKILL.md"},{"name":"ima-sdk-client-side","description":"Supports Interactive Media Ads (IMA) SDK. Use this skill for client-side ad insertion when you are requesting video ads for websites, apps, TVs or other platforms using VAST or VMAP. Do not use for Dynamic Ad Insertion (DAI), SSAI, or SGAI.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/ads/ima-sdk-client-side/SKILL.md"},{"name":"managed-airflow-dag-authoring","description":"Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/managed-airflow-dag-authoring/SKILL.md"},{"name":"managed-airflow-dag-troubleshooting","description":"Provides guidance for troubleshooting Apache Airflow DAGs (failed DAG runs and task instances) in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Use when figuring out reasons for DAG run or task instance failures. Don't use when looking for overall recommendations for Managed Airflow environment performance.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/managed-airflow-dag-troubleshooting/SKILL.md"},{"name":"managed-airflow-migrations","description":"Provides guidance for migrating Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers migration to Airflow 2.11.1 (MSAA Gen 2 and 3) and Airflow 3 (MSAA Gen 3), including environment inspection, GCS download/upload and scanning patterns for breaking changes. Use when migrating the DAG code to newer Airflow version. Don't use when checking DAG run failures unrelated to code migration.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/managed-airflow-migrations/SKILL.md"},{"name":"retrieving-developer-knowledge","description":"Searches, retrieves, and synthesizes official Google developer documentation across Google Cloud, AI/Gemini, Android, Chrome, Web, Flutter, Go, Firebase, and other Google developer platforms. Integrates with the Developer Knowledge MCP server (search_documents, get_documents, answer_query) or the Developer Knowledge REST API fallback. Use when searching for gcloud CLI commands, API syntax, IAM permissions, official documentation, architectural comparisons, or product choice overviews. Don't use for local filesystem lookups or non-Google documentation.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/developers/retrieving-developer-knowledge/SKILL.md"},{"name":"spanner-basics","description":"Assists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/spanner-basics/SKILL.md"},{"name":"workload-manager-basics","description":"Use this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM auditing.","entrypoint":"https://raw.githubusercontent.com/google/skills/main/skills/cloud/workload-manager-basics/SKILL.md"}]}