skills

In the author's own words, from each skill's SKILL.md — showing the first 30 of 35 files.

• ai-inference-deploy

  Top-level router for an Intel GPU inference request that has not settled what it wants yet. Use for the bare ask — "deploy qwen", "serve a model on my Arc", "get llama running on my Intel graphics card", "which one do I use, vllm or sglang?", "what do I need to run this model on my GPU?" — where the runtime, the host OS, the install path and the readiness of the machine are all still open. It pic…

  skills/ai-inference-deploy/SKILL.md on GitHub (opens in a new tab)

• cuda-to-xpu-migration

  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" a…

  skills/cuda-to-xpu-migration/SKILL.md on GitHub (opens in a new tab)

• dpnp-interop

  Passing dpnp arrays to and from other Python libraries on Intel CPUs and GPUs. Use when dpnp numeric work has to feed pandas, scikit-learn, PyTorch, or TensorFlow, when one of those libraries raises a type error on a dpnp array, when a pipeline mixes device math with host-only libraries, or when the user asks where in a pipeline the conversion belongs. Covers the boundary conversion pattern per l…

  skills/dpnp-interop/SKILL.md on GitHub (opens in a new tab)

• dpnp-io

  Reading and writing files from dpnp code on Intel CPUs and GPUs. Use when the user needs to load an array into dpnp or save a dpnp result — .npy, .npz, HDF5 via h5py, Zarr, CSV or plain text — when a file is larger than device memory and has to be read in chunks, or when they ask why dpnp has no save function of its own. Covers the NumPy conversion round trip, chunked and incremental patterns, an…

  skills/dpnp-io/SKILL.md on GitHub (opens in a new tab)

• dpnp-linalg-fft

  Linear algebra and FFT with dpnp on Intel CPUs and GPUs, backed by oneMKL. Use when a matrix multiply, solve, decomposition, eigenvalue problem, or Fourier transform is the hot part of NumPy code, when the user asks whether dpnp covers a linalg or FFT call, when an FFT result differs slightly from NumPy's, or when a large transform runs out of device memory. Covers the supported surface, transfor…

  skills/dpnp-linalg-fft/SKILL.md on GitHub (opens in a new tab)

• dpnp-memory

  Device memory management for dpnp arrays on Intel CPUs and GPUs. Use when a dpnp script grows in memory until it fails, when a dataset does not fit in device memory, when an array turns out to be on a different device than expected, or when a loop allocates a new array on every iteration. Covers USM allocation, inspecting placement and queues with dpctl, reusing an output buffer, chunking a workl…

  skills/dpnp-memory/SKILL.md on GitHub (opens in a new tab)

• dpnp-migration

  Porting an existing NumPy or CuPy program to dpnp on Intel CPUs and GPUs. Use when deciding whether a codebase can run on dpnp at all, when a call raises NotImplementedError or AttributeError after the import was swapped, when the user asks whether dpnp supports a specific NumPy function or family, or when CuPy code has to move to Intel hardware. Covers probing the installed release for what it a…

  skills/dpnp-migration/SKILL.md on GitHub (opens in a new tab)

• dpnp-quickstart

  NumPy-compatible array operations optimized for Intel hardware. Use when the user wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp against NumPy. Covers install, device control, fallback patterns, and pro…

  skills/dpnp-quickstart/SKILL.md on GitHub (opens in a new tab)

• dpnp-random

  Random number generation with dpnp on Intel CPUs and GPUs, backed by oneMKL. Use when NumPy random calls move to dpnp, when a seeded dpnp run does not reproduce a NumPy sequence, when a distribution turns out not to be implemented, when results have to be reproducible across machines, or when random data feeds a training or augmentation loop. Covers the supported distributions, what seeding does…

  skills/dpnp-random/SKILL.md on GitHub (opens in a new tab)

• dpnp-troubleshooting

  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises NotImplementedError or an unexpected TypeError, when the import fails or a SYCL runtime library is missing, when no SYCL device is visible, when dpctl reports a device the user did not expect, or when dpnp code runs slower than the NumPy it replaced. Covers the fallback pattern for unimplemented APIs, install repair, forcing CP…

  skills/dpnp-troubleshooting/SKILL.md on GitHub (opens in a new tab)

• linux-perf

  Profile and fix Linux performance problems using `perf`. Workflows: (A) hardware counters -- IPC, cache-miss, branch mispredictions; (B) hotspot profiling -- which functions and source lines consume CPU, with SIMD and accumulator detection; (C) cache-line contention -- false sharing, HITM, `perf c2c`; (D) core-count scaling -- dual-profile comparison, bottleneck categorization; (E) structured hot…

  skills/linux-perf/SKILL.md on GitHub (opens in a new tab)

• llamacpp-xpu-run

  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. Use when the user has a GGUF model and wants f…

  skills/llamacpp-xpu-run/SKILL.md on GitHub (opens in a new tab)

• mkl-extension-advisor

  Deciding whether Intel's MKL extension packages apply to NumPy or SciPy code on Intel CPUs. Use when a user asks whether mkl_fft, mkl_random, or mkl_umath help their code, or points at a snippet, function, file, or codebase using np.fft, scipy.fft, np.random, or element-wise math ufuncs. Also use to check whether these extensions are already active in an environment, to fix an install so they and…

  skills/mkl-extension-advisor/SKILL.md on GitHub (opens in a new tab)

• model-can-it-fit

  Estimate whether a Hugging Face decoder-only LLM, MoE, or VLM 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, activations, framework overhead, and first mitigation. Not for diffusion. Memory-only — does NOT predict throughput, tokens/sec, latency, or…

  skills/model-can-it-fit/SKILL.md on GitHub (opens in a new tab)

• model-config-recommend

  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; predi…

  skills/model-config-recommend/SKILL.md on GitHub (opens in a new tab)

• onetbb-quickstart

  Getting started with Intel oneTBB for C++ parallelism on Intel CPUs. Use when a C++ loop or reduction should run on multiple threads with oneTBB, including a request to make an existing loop or function use all cores with oneTBB or TBB; when the user needs the headers, namespace, or CMake wiring for a first oneTBB program; when a parallel_for body has a data race; or when a reduction is accumulat…

  skills/onetbb-quickstart/SKILL.md on GitHub (opens in a new tab)

• openvino-gpu-run

  Serve an LLM on an Intel GPU with OpenVINO Model Server (OVMS) behind an OpenAI-compatible API — /v1/chat/completions and /v1/models. The default serving path for Intel integrated graphics (Core Ultra laptop GPUs that share system RAM), and the one runtime in this catalog with a native Windows build; it also runs on discrete Intel GPUs. Covers picking a model source (pre-converted OpenVINO models…

  skills/openvino-gpu-run/SKILL.md on GitHub (opens in a new tab)

• performance-patterns

  Detect and fix x86/C/C++ performance patterns from source code or profiling output (perf, VTune, flamegraphs). Invoke when the user asks to optimize, review for performance, or write new SIMD/vectorized code — even without profiling data. Trigger on: serial accumulator loops, narrow SIMD (xmm/ymm that could be ymm/zmm), _mm* intrinsics, HITM/cmpxchg clusters, false sharing, missing restrict or vz…

  skills/performance-patterns/SKILL.md on GitHub (opens in a new tab)

• phoronix-test-suite

  Install, run, parse, and optimize benchmarks from the Phoronix Test Suite (PTS). Use this skill whenever the user mentions "phoronix", "pts/", or "phoronix-test-suite", or asks to run, measure, improve, or optimize a PTS test — e.g., "run pts/mt-dgemm", "optimize pts/compress-zstd", "what score does pts/x265 get". Trigger immediately on any `pts/<testname>` reference, even if the user doesn't exp…

  skills/phoronix-test-suite/SKILL.md on GitHub (opens in a new tab)

• sglang-xpu-bench

  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).

  skills/sglang-xpu-bench/SKILL.md on GitHub (opens in a new tab)

• sglang-xpu-run

  Serve a Hugging Face safetensors model on an Intel GPU using SGLang's XPU backend with the OpenAI-compatible API. Covers pulling the official `lmsysorg/sglang:v0.5.20-xpu` release image, 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…

  skills/sglang-xpu-run/SKILL.md on GitHub (opens in a new tab)

• torch-xpu-bench

  Benchmark a Hugging Face model on an Intel GPU through pure PyTorch + Transformers, **single-process, no HTTP server**. Measures generate() throughput in tokens/sec, time-to-first-token, decode-step latency, and peak XPU memory. Also covers diffusion and encoder-only models via `references/non-llm-snippets.md`. Use after **model-can-it-fit** to validate predicted memory against `torch.xpu.max_mem…

  skills/torch-xpu-bench/SKILL.md on GitHub (opens in a new tab)

• torch-xpu-profile

  Profile a Hugging Face model on Intel GPU at the **PyTorch level** with `torch.profiler` and Kineto. Captures CPU + XPU timeline, exports Chrome trace, identifies hottest kernels and async-overlap gaps. Use when the user asks why a model is slow, which op is the bottleneck, or where the GPU is idle. Not for profiling inside a running vLLM server (use vllm-xpu-profile) or for SYCL-kernel-level sig…

  skills/torch-xpu-profile/SKILL.md on GitHub (opens in a new tab)

• torch-xpu-run

  Run an arbitrary Hugging Face safetensors model on an Intel GPU using **upstream PyTorch** (>= 2.8) with the built-in `torch.xpu` device. Covers loading from the Hub, picking the right dtype, autocast, multi-GPU with accelerate's `device_map`, and the CUDA -> XPU code translation a user has to do once. Use for the Transformers / Accelerate / Diffusers path. Not for OpenAI-compatible serving (use…

  skills/torch-xpu-run/SKILL.md on GitHub (opens in a new tab)

• vllm-xpu-bench

  Benchmark a **running vLLM-XPU OpenAI-compatible server** on an Intel GPU using `vllm bench`. Measures TTFT (time-to-first-token), TPOT (time-per-output-token), ITL (inter-token latency), end-to-end latency, and throughput under concurrency. Covers online (`vllm bench serve`) and offline (`vllm bench throughput`) modes; concurrency sweeps and quant comparison live in `references/sweep-and-compare…

  skills/vllm-xpu-bench/SKILL.md on GitHub (opens in a new tab)

• vllm-xpu-profile

  Profile a running vLLM-XPU server with torch.profiler around a window of real requests, either via /start_profile and /stop_profile HTTP endpoints or via vllm bench --profile for offline runs. Use to find the dominant op under real concurrent traffic. Not for pure PyTorch (use torch-xpu-profile), SYCL kernel-level signal (use xpu-profile-unitrace), throughput numbers (use vllm-xpu-bench), or non-…

  skills/vllm-xpu-profile/SKILL.md on GitHub (opens in a new tab)

• vllm-xpu-run

  Serve a Hugging Face safetensors model on an Intel GPU with upstream vLLM-XPU's OpenAI-compatible API, or check whether a model or architecture is currently documented on XPU. Covers live support lookup, image choice, container launch, known serve-flag requirements, model-impl fallback, and attention/quant compatibility. Use to launch /v1/chat/completions or /v1/completions, troubleshoot a launch…

  skills/vllm-xpu-run/SKILL.md on GitHub (opens in a new tab)

• xpu-container-run

  Launch a Docker container with Intel GPU access on Linux. Encodes the correct combination of `--device /dev/dri`, render-group access, `--ipc=host`, `ZE_AFFINITY_MASK` pinning, Hugging Face cache mount, and `--entrypoint /bin/bash` for interactive use. Use when running any Intel-XPU container (vLLM-XPU, sglang-xpu, torch-XPU, llama.cpp SYCL, etc.) and the device must be visible inside. The CUDA a…

  skills/xpu-container-run/SKILL.md on GitHub (opens in a new tab)

• xpu-deploy-plan

  Plan an end-to-end Intel XPU model deployment by chaining existing skills. Calls xpu-runtime-preflight (readiness), model-can-it-fit (sizing), model-config-recommend (flags), and the selected runtime skill (vllm-xpu-run / sglang-xpu-run / torch-xpu-run), then writes a single PLAN.md with one exact launch command, smoke test, and rollback to .out/skills/xpu-deploy-plan/. Use when the user asks for…

  skills/xpu-deploy-plan/SKILL.md on GitHub (opens in a new tab)

• xpu-discover

  Inventory Intel GPUs (Arc, Arc Pro) on a Linux host. Detect devices, collect driver, firmware and component-health evidence, list processes using each XPU, and read live utilisation.

  skills/xpu-discover/SKILL.md on GitHub (opens in a new tab)

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