Create a CUDA-to-XPU migration assessment for an existing AI repo. Identify CUDA-specific assumptions, route to the right XPU skills, produce a migration report. Use when the user has a CUDA repo, notebook, Dockerfile, launch script, HF / vLLM / SGLang workload, or Triton kernel and asks to migrate it to Intel Arc / Arc Pro / Battlemage / XPU — including "convert this to XPU" / "move it to XPU" and the bare "migrate this repo" request where scope is not yet set. A request that says "port" routes to xpu-port. Plans and routes only. Not for executing an already-scoped rewrite (use xpu-port), running migrated code, or measuring it.
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Deploy a Prometheus + Grafana + XPU Manager monitoring stack on an Intel GPU host using the intel/xe-full-stack-monitoring repo (plain Docker Compose). Brings up containers for real-time GPU, CPU, and vLLM metrics collection and visualization. Use when the user wants to monitor XPU utilization, temperature, power, memory, or vLLM inference performance.
1 skill
Run a GGUF model on an Intel GPU using llama.cpp's SYCL backend (Level Zero) with the official intel.Dockerfile. Covers building the Docker image from source at a pinned tag, launching llama-server with an OpenAI-compatible API, device selection, multi-GPU layer splitting, all recommended runtime env vars, flash-attention, and quantisation selection.
1 skill
Estimate whether a Hugging Face decoder-only LLM, MoE, VLM, sliding-window, or hybrid state-space (SSM) model fits in Intel GPU VRAM for a quantization, context length, concurrency, runtime, and tensor-parallel setting. Use for memory-fit or max-model-len planning before launch. Reports weights, KV cache, recurrent state, activations, framework overhead, and first mitigation. Not for diffusion. Memory-only — does NOT predict throughput, tokens/sec, latency, or runtime config; route those to bench/deploy/recommend skills.
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Recommend how to configure vLLM-XPU for a Hugging Face decoder-only LLM on Intel Arc B-series GPUs: choose quantization, KV dtype, DP/TP layout, max concurrency, and max context using roofline math against published hardware specs. Use when the user asks "How should I configure vLLM?", requests the best vLLM configuration, or asks how to use one or multiple Arc cards. Must run recommend.py; predictions are physics-bounded ranges, not measured throughput. Use after xpu-discover and before vllm-xpu-run.
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Benchmark a **running SGLang-XPU server** on an Intel GPU using `sglang.bench_serving`. Measures TTFT, TPOT, ITL, end-to-end latency, and throughput against the OpenAI-compatible endpoint. Use after sglang-xpu-run. Not for vLLM servers (use vllm-xpu-bench) or no-server PyTorch (use torch-xpu-bench).
1 skill