Intel GPU AI Skills is a collection of agent skills for setting up, running, benchmarking, and profiling Hugging Face models on Intel GPUs. It supports workflows involving PyTorch, vLLM-XPU, SGLang-XPU, llama.cpp-SYCL, and migration from CUDA to XPU. The catalogue contains the project's skills, instructions, agent, and plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add intel/gpu-ai-skills --skill cuda-to-xpu-migrationgit clone --depth 1 https://github.com/intel/gpu-ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/intel/gpu-ai-skills/cuda-to-xpu-migration)<a href="https://agentmods.dev/skills/intel/gpu-ai-skills/cuda-to-xpu-migration"><img src="https://agentmods.dev/badge/skills/intel/gpu-ai-skills/cuda-to-xpu-migration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/intel/gpu-ai-skills/cuda-to-xpu-migration"><img src="https://agentmods.dev/badge/skills/intel/gpu-ai-skills/cuda-to-xpu-migration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00163 | $0.02313 |
| Opus 5 | $0.00081 | $0.01156 |
| Sonnet 5 | $0.00033 | $0.00463 |
| Haiku 4.5 | $0.00016 | $0.00231 |
Grade A, and why
cuda-to-xpu-migration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cuda-to-xpu-migration
Create a migration assessment for CUDA-oriented repos that want an Intel XPU path.
Scope
This skill is for assessment and planning only.
- Identify CUDA-specific assumptions and likely migration surfaces.
- Reuse existing skills for code translation, runtime setup, serving, profiling, and sizing.
- Produce a clean migration report with recommended edits, blockers, and next steps.
- Do not execute or validate migrated code.
- Do not duplicate detailed commands or framework runbooks that are already covered by other skills.
Always assess; route forward once
This skill assesses the workflow verbs — "assess", "migrate", "convert", "move" — with the same read-only pass: inventory, classify, report. A request that says "port" (or otherwise names the rewrite explicitly) belongs to xpu-port, not here; route it there. For the workflow verbs, do not bounce the request away before the report exists: a mis-routed execution request costs one cheap read-only assessment, while the reverse mis-route would rewrite a user's repo unasked.
The report's Next steps (with the "Non-namespace CUDA surfaces → Route" table) is the single onward-routing authority. It names the executor for each surface — xpu-port for portable Python, xpu-deploy-plan for API-first / serving repos, container and runtime skills per tier. Routing flows one way, assess → execute; nothing here routes backward.
Use with
- xpu-discover for an XPU environment / driver preflight check before recommending a migration target.
- xpu-runtime-preflight for a read-only go/no-go check of host/container XPU readiness (drivers, /dev/dri, permissions, runtime, and essentials) before any XPU workload.
- torch-xpu-run for PyTorch / Transformers CUDA-to-XPU code translation guidance.
- xpu-container-run for container runtime setup on Intel GPU.
- vllm-xpu-run for vLLM launch and runtime guidance.
- sglang-xpu-run for SGLang launch and runtime guidance.
- model-can-it-fit and model-config-recommend for fit and config decisions.
- xpu-deploy-plan for a coordinated end-to-end serving plan (preflight → fit → config → launch → smoke test) once the assessment routes an API-first / serving-stack repo to a local XPU endpoint.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 113 lines · 163 tokens per session scan A 2ee82ad4f1c3
cuda-to-xpu-migration is a skill published in the GitHub repository intel/gpu-ai-skills (21 stars, last pushed 5d ago), licensed Apache-2.0. It adds 163 tokens to every session and 2,313 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
minicpm5-deploy-vllm-ascend
Deploy MiniCPM5-2B with vLLM on Huawei Ascend NPU using vLLM-Ascend. Use when the user mentions vLLM-Ascend, Ascend NPU, Huawei Ascend, CANN, torchnpu, davinci devices, or wants an OpenAI-compatible MiniCPM5 server on Ascend hardware.
amc-run-video-calibration
Calibrates pre-recorded cam.mp4 datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to amc-run-rtsp-calibration.