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Found 332 Skills
Use when configuring QueryClient, implementing mutations, debugging performance, or adding optimistic updates with @tanstack/react-query in Next.js App Router. Covers factory patterns, query keys, cache invalidation, observer debugging, HydrationBoundary, multi-layer caching. Keywords TanStack Query, useSuspenseQuery, useQuery, useMutation, invalidateQueries, staleTime, gcTime, refetch, hydration.
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (see golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill).
Next.js 15 App Router patterns. Trigger: When working in Next.js App Router (app/), Server Components vs Client Components, Server Actions, Route Handlers, caching/revalidation, and streaming/Suspense.
Guidance for setting up HuggingFace model inference services with Flask APIs. This skill applies when downloading HuggingFace models, creating inference endpoints, or building ML model serving APIs. Use for tasks involving transformers library, model caching, and REST API creation for ML models.
System architecture skill for designing scalable, maintainable software systems. Covers microservices/monolith decisions, API design, DB selection, caching, security, and scalability planning.
This skill provides comprehensive knowledge for TanStack Query v5 (React Query) server state management in React applications. It should be used when setting up data fetching with useQuery, implementing mutations with useMutation, configuring QueryClient, managing caching strategies, migrating from v4 to v5, implementing optimistic updates, using infinite queries, or encountering query/mutation errors. Use when: initializing TanStack Query in React projects, configuring QueryClient settings, creating custom query hooks, implementing mutations with error handling, setting up optimistic updates, using useInfiniteQuery for pagination, migrating from React Query v4 to v5, debugging stale data issues, fixing caching problems, resolving v5 breaking changes, implementing suspense queries, or setting up query devtools. Keywords: TanStack Query, React Query, useQuery, useMutation, useInfiniteQuery, useSuspenseQuery, QueryClient, QueryClientProvider, data fetching, server state, caching, staleTime, gcTime, query invalidation, prefetching, optimistic updates, mutations, query keys, query functions, error boundaries, suspense, React Query DevTools, v5 migration, v4 to v5, request waterfalls, background refetching, cacheTime renamed, loading status renamed, pending status, initialPageParam required, keepPreviousData removed, placeholderData, query callbacks removed, onSuccess removed, onError removed, object syntax required
When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.
Instruction set for enabling and operating the Spring Cache abstraction in Spring Boot when implementing application-level caching for performance-sensitive workloads.
Use when serving uploaded files to users. Covers API-proxied file serving, direct storage URLs (S3/R2/Cloudinary), CDN configuration, public file URLs, caching headers, image optimization with Cloudinary, and serving files in frontend applications.
Ruby on Rails performance and maintainability optimization guidelines for building backend APIs and frontend web applications. This skill should be used when writing, reviewing, or refactoring Ruby on Rails code to ensure optimal patterns for controllers, models, ActiveRecord queries, caching, views, API design, security, and background jobs. Triggers on tasks involving Rails controllers, ActiveRecord queries, migrations, Turbo/Hotwire, API endpoints, background jobs, or Rails performance improvements.