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.
1 skill
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.
1 skill
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
Serve a Hugging Face safetensors model on an Intel GPU using SGLang's XPU backend with the OpenAI-compatible API. Pins the validated `lmsysorg/sglang:v<version>-xpu` release image and flags newer unvalidated releases, then covers the container flag set for Intel DRM devices, the UMD/kernel pairing the image pins, the `--device xpu --attention-backend intel_xpu` flag set, page-size and quantization constraints, multimodal serving, and how to validate output content (not just HTTP 200). Use when the user needs SGLang's RadixAttention prefix caching or grammar-constrained output; for broad-coverage serving on Intel today prefer vllm-xpu-run, and for benchmarking a running server use sglang-xpu-bench.
1 skill