Browse reusable Agent Skills, each with a clear purpose and practical guidance.
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.
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.
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 when users need holistic, end-to-end design recommendations and architectural guidance for complex workloads on Google Cloud for specific use cases. Don't use this skill when other specialized skills exist that directly address the user-specified workload or use case. If the user's request is narrowly focused on a specific Google Cloud product, use the skills that are specific to that product. When users need assistance with workflows for onboarding, authentication, or designing foundational infrastructure, use the recipe skills that are specific to those workflows.
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.
Provides instructions to implement, integrate, or configure Google Mobile Ads (GMA) banner ads in Android and iOS mobile applications. Use when the task involves setting up banner ads in a mobile application.
Roll Playwright Python to a new driver version. Walks the upstream `docs/src/api/` commit range, ports each public-API change, suppresses the rest in `expected_api_mismatch.txt`, regenerates the typed surface, and adds tests.
This skill should be used when the user asks to "merge a PR", "review and merge pull requests", "integrate external contributions", "handle PR conflicts", "cherry-pick from a PR", or needs to merge GitHub PRs while maximizing contributor attribution.
React bindings for TanStack Router: RouterProvider, useRouter, useRouterState, useMatch, useMatches, useLocation, useSearch, useParams, useNavigate, useLoaderData, useLoaderDeps, useRouteContext, useBlocker, useCanGoBack, Link, Navigate, Outlet, CatchBoundary, ErrorComponent. React-specific patterns for hooks, providers, SSR hydration, and createLink with forwardRef.
TanStack Router bundler plugin for route generation and automatic code splitting. Supports Vite, Webpack, Rspack, and esbuild. Configures autoCodeSplitting, routesDirectory, target framework, and code split groupings.
Step-by-step migration from Next.js App Router to TanStack Start: route definition conversion, API mapping, server function conversion from Server Actions, middleware conversion, data fetching pattern changes.