What problem does it solve? Production systems suffer from slow APIs, poor Core Web Vitals, and costly cloud configurations, but teams often optimize blindly without measurement. This Skill provides a measurement-first reference for diagnosing and fixing performance bottlenecks across Python backends, Next.js frontends, and Azure cloud infrastructure. ## Core Features & Use Cases - Python Performance: Profiling workflows with py-spy and Scalene, GIL and free-threading guidance for Python 3.13t/3.14t, NumPy/Pandas vectorization (100–740x wins), Numba/Cython acceleration, asyncio best practices, and ASGI vs WSGI benchmarks. - Next.js/TypeScript Performance: Core Web Vitals targets (LCP, INP, CLS), RSC bundle discipline, App Router's four caching layers, PPR/Cache Components, Turbopack trade-offs, and Prisma connection pooling in serverless. - Azure Cloud Performance: Compute tier cold-start comparison, ACA/KEDA scale-to-zero, messaging service selection (Event Grid vs Event Hubs vs Service Bus), Redis ConnectionMultiplexer rules, Front Door CDN/WAF, VMSS autoscale thresholds, and cost tier guidance. - Use Case: Your Next.js dashboard has INP over 200ms and your Python API's P99 latency is spiking. Use this Skill to identify that a misplaced "use client" directive is bloating the bundle and that a copy.deepcopy in a retry loop is the Python hot path, then apply the staged remediation plan. ## Quick Start Ask the AI to diagnose why your FastAPI endpoint has high P99 latency and recommend profiling steps and fixes based on the performance reference.
