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Found 332 Skills
Use when building an LLM-powered app that needs cost control via model routing, budget tracking, retry, and prompt caching.
Provides comprehensive Turborepo monorepo management guidance for TypeScript/JavaScript projects. Use when creating Turborepo workspaces, configuring turbo.json tasks, setting up Next.js/NestJS apps, managing test pipelines (Vitest/Jest), configuring CI/CD, implementing remote caching, or optimizing build performance in monorepos
Optimize web performance through network protocols, resource loading, and browser rendering internals. Use when the user mentions "page load speed", "Core Web Vitals", "HTTP/2", "resource hints", "network latency", or "render blocking". Covers TCP/TLS optimization, caching strategies, WebSocket/SSE, and protocol selection. For UI visual performance, see refactoring-ui. For font loading, see web-typography.
Isar Database, Caching & Offline-First Patterns
SWR data-fetching expert guidance. Use when building React apps with client-side data fetching, caching, revalidation, mutations, optimistic UI, pagination, or infinite loading using the SWR library.
In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure.
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
Guidelines for developing GraphQL APIs and React applications using Apollo Client for state management, data fetching, and caching
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
Integrate Portkey AI Gateway into TypeScript/JavaScript applications. Use when building LLM apps with observability, caching, fallbacks, load balancing, or routing across 200+ LLM providers.
Optimizes API performance through payload reduction, caching strategies, and compression techniques. Use when improving API response times, reducing bandwidth usage, or implementing efficient caching.
When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.