Inference-AIops

Use this skill whenever the user needs to operate a GPU inference cluster — vLLM (OpenAI API + Prometheus /metrics) and Ray Serve / Ray Jobs (Ray dashboard): a one-shot cluster overview (deployments + total replicas + queue backpressure), request metrics (TTFT / TPOT / e2e latency + token totals), queue depth, KV-cache stats (utilisation, prefix-cache hit rate, preemptions), the flagship latency root-cause analysis (diagnose_latency_spike) and low-utilisation RCA, Ray Serve autoscaling and scaling (scale up/down, scale-to-zero, drain a replica), LoRA load/unload, base-model hot-swap, deploy/undeploy/redeploy, prefix-aware routing, GPU utilisation, Ray jobs, and cost per million tokens. Always use this skill for "why is inference slow", "TTFT spike", "latency spike", "GPU underutilised", "scale down the deployment", "scale to zero", "drain a replica before a reboot", "hot-swap the base model", "load a LoRA adapter", "KV cache pressure", "prefix cache hit rate", "queue backpressure", "autoscale config", or "cos