Intel GPU AI Skills

A collection of Agent Skills for setting up, running, benchmarking, and profiling arbitrary Hugging Face safetensors and GGUF models on Intel GPUs (Arc, Arc Pro, Battlemage, Data Center GPU Max). Covers the PyTorch + Transformers, vLLM-XPU, SGLang-XPU, and llama.cpp-SYCL stacks, plus CUDA to XPU migration.

Format follows the Agent Skills specification. Ships as the intel-model-skillpack plugin.

Skills

Setup — get the host ready

Skill	Useful for	Example prompt
xpu-system-setup	First-time host setup: installs xpu-smi, Level Zero runtime, Docker, configures the render group and Intel GPU PPA. Also fixes Battlemage-specific prerequisites.	"Prepare this bare-metal Ubuntu box for Intel GPU work."
xpu-discover	Inventory Intel GPUs and check driver health (xpu-smi wrapper). CUDA analogue is nvidia-smi.	"Is my Intel GPU detected? Run a quick health check."
xpu-runtime-preflight	Check shared host, device, Docker, proxy, storage, and optional container readiness before using GPU/XPU skills in this pack.	"Before using the XPU skills on this host, run the preflight and tell me what blocks it."
xpu-container-run	Launch a Docker container with Intel GPU access (/dev/dri, render group, ZE_AFFINITY_MASK, --ipc=host).	"How do I launch a Docker container that can see my Intel GPU?"

Plan — size and configure before you launch

Skill	Useful for	Example prompt
model-can-it-fit	Estimate VRAM (weights + KV + activations + framework) from HF config.json. Decoder-only LLM, MoE, VLM; refuses diffusion.	"Will Qwen2.5-32B in int4 fit on my Arc Pro B70 at 8K context, concurrency 4?"
model-config-recommend (experimental)	Recommend a vLLM-XPU deployment config (quant, KV dtype, DP/TP, capacity) using roofline math against Intel Arc B-series specs. Predict → calibrate → verify.	"What's the best config to serve Qwen2.5-7B on my B70 at 8K?"
xpu-deploy-plan	Chain preflight, fit, config-recommend, and a runtime skill into one PLAN.md with an exact launch command, smoke test, and rollback.	"Give me a coordinated deployment plan for Llama-3-8B on this box."

Run — get a model on the GPU

Skill	Useful for	Example prompt
xpu-model-type-detect	Detect a Hugging Face model's actual type (text-gen, VLM, diffusion, encoder, etc.) before loading, so the agent picks the right AutoModel class and input kwargs.	"What kind of model is this, and how should I load it?"
torch-xpu-run	Run any HF safetensors model via upstream PyTorch + Transformers + torch.xpu. CUDA → XPU code translation.	"Run gemma-3 in pure PyTorch on my Intel GPU."
vllm-xpu-run	Serve a model with vLLM-XPU's OpenAI-compatible HTTP API. Image picker, flag rationales, multi-GPU patterns.	"Start a vLLM server with Qwen2.5-7B on my Intel GPU."
sglang-xpu-run	Serve a model with SGLang's XPU backend (--device xpu --attention-backend intel_xpu). RadixAttention prefix caching.	"Serve Qwen3 with sglang on my Battlemage GPU."
llamacpp-xpu-run	Run a GGUF model via llama.cpp's SYCL backend with an OpenAI-compatible llama-server; no Python/PyTorch required.	"Serve this GGUF model locally on my Arc GPU with llama.cpp."

Benchmark — measure how fast

Skill	Useful for	Example prompt
torch-xpu-bench	Single-process bench of an HF model via pure PyTorch (no server). TTFT, decode rate, peak XPU memory.	"Bench Qwen3-8B forward pass on Intel without any server."
vllm-xpu-bench	Bench a running vLLM-XPU server (vllm bench serve / throughput). TTFT, TPOT, ITL, throughput at concurrency.	"Benchmark TTFT and TPOT on my running vLLM-XPU server."
sglang-xpu-bench	Bench a running SGLang server (sglang.bench_serving). Includes prefix-cache hit-rate measurement.	"Measure RadixAttention prefix-cache hit rate on my sglang server."

Profile — find why it's slow

Skill	Useful for	Example prompt
torch-xpu-profile	Profile an HF model with torch.profiler + Kineto. Export Chrome trace; find hot ops + idle gaps.	"Why is my Qwen2.5 generate() slow on Intel?"
vllm-xpu-profile	Profile a running vLLM server via /start_profile and /stop_profile, or offline vllm bench --profile.	"Capture a vLLM-XPU profile around a real-traffic window."
xpu-profile-unitrace	SYCL / Level Zero kernel-level profiling with unitrace (PTI-GPU; built from source). Per-kernel timing, oneCCL events, HW counters.	"Show me the actual SYCL kernel names taking the time."

Migrate — bring an existing CUDA project over

Skill	Useful for	Example prompt
cuda-to-xpu-migration	Assess a CUDA repo, notebook, Dockerfile, or launch script; produce a migration report and route to the right XPU skills. Plans and routes only.	"Migrate this CUDA training repo to Intel XPU."
xpu-port	Execute a single-target CUDA → XPU port: libcst-based scan, mechanical rewrite, and CPU FP64 vs. target-dtype correctness verification.	"Port my repo at ./my-model to XPU now."

The descriptions are designed to disambiguate by deployment shape: "benchmark X" alone is intentionally ambiguous (which framework?), so a good agent will ask whether you mean PyTorch / vLLM / sglang before picking. If you want to be explicit, mention the skill by name: "Use the vllm-xpu-bench skill to ...".

Installing

These skills work with any agent that supports the Agent Skills standard, including Claude Code, opencode, OpenAI Codex, Cursor, GitHub Copilot CLI, Gemini CLI, Qwen Code, Kimi Code, Hermes Agent, and OpenClaw.

Local clone, all agents at once (recommended)

git clone https://github.com/intel/gpu-ai-skills.git intel-model-skillpack
cd intel-model-skillpack
bash scripts/install.sh

Installs into every agent skills directory it detects. Pass --all to also create dirs for agents you haven't used, --agent <name> for one agent, --uninstall to reverse.

Claude Code

/plugin marketplace add intel/gpu-ai-skills
/plugin install <skill-name>@intel-model-skillpack

Gemini CLI

This repo ships gemini-extension.json:

gemini extensions install . --consent
# or from URL:
gemini extensions install https://github.com/intel/gpu-ai-skills.git --consent

Clone / Copy

For any other agent, clone this repo and copy the skill folders into the agent's skills directory:

Agent	Skill Directory	Docs
Claude Code	~/.claude/skills/	docs
opencode	~/.config/opencode/skills/	docs
OpenAI Codex	${CODEX_HOME:-~/.codex}/skills/	docs
GitHub Copilot CLI	~/.copilot/skills/	docs
Cursor	~/.cursor/skills/	docs
Qwen Code	~/.qwen/skills/	docs
Kimi Code	~/.kimi/skills/	docs
Hermes Agent	~/.hermes/skills/ or hermes skills tap add intel/gpu-ai-skills	docs
OpenClaw	~/.openclaw/skills/, <workspace>/skills/, or skills.load.extraDirs	docs
Generic / AGENTS.md	<repo>/.agents/skills/ or ~/.config/agents/skills/	uses agents/AGENTS.md

cp -r plugins/intel-model-skillpack/skills/* <skill-directory>/

opencode also auto-loads from ~/.claude/skills/ and ~/.agents/skills/, so any of those paths works. GitHub Copilot CLI also accepts gh skill install intel/gpu-ai-skills --agent github-copilot --scope user (gh ≥ v2.90).

Layout

plugins/intel-model-skillpack/skills/   # one directory per skill
scripts/               # repo tooling (install, publish, generate AGENTS.md)
agents/                # generated AGENTS.md fallback bundle
.claude-plugin/        # Claude Code plugin marketplace manifests
tests/                 # static validation
template/              # SKILL.md template for contributors

The agents/AGENTS.md bundle is generated from the individual SKILL.md files; re-run scripts/ tooling after adding a skill to keep it fresh.

Ubuntu 24.04 + Arc Pro B60/B70 (Battlemage)

On Ubuntu 24.04 with the stock kernel, Battlemage GPUs require three prerequisites before the skills work. Run the diagnostic first:

bash plugins/intel-model-skillpack/skills/xpu-system-setup/scripts/check_battlemage_prerequisites.sh

It checks for nomodeset in GRUB, the OEM kernel 6.17 requirement, and compute runtime >=26.18. Pass --fix to apply remediations, or follow the steps in plugins/intel-model-skillpack/skills/xpu-system-setup/SKILL.md → Battlemage Prerequisites.

Contributing

New skills go under plugins/intel-model-skillpack/skills/<skill-name>/ with a SKILL.md. Start from template/SKILL.md.

Before committing any skill change:

bash scripts/check-skills.sh

See CONTRIBUTING.md for the validation rules.

Validate

bash tests/static.sh

The checks themselves are stdlib-only. On this development branch it also runs guardrails/check.py, which needs PyYAML — without it that one step exits PyYAML is required: pip install pyyaml and it fails:

python3 -m pip install pyyaml

Unit tests for the VRAM calculator, which need pytest:

python3 -m pip install pytest
python3 -m pytest tests/test_fit.py -q      # 71 passed, 6 skipped without HF_TOKEN

These fetch each model's upstream config.json from the Hub at a pinned revision on first use and cache it under tests/data/.cache/. The configs are third-party files under their own licences and are deliberately not committed to this repository; a config that cannot be fetched skips its tests rather than failing them. To pre-fetch the configs and then run with no network, see Layer 3a in HOW_TO_TEST.md.

For deeper testing — install round-trip, per-skill acceptance, end-to-end smoke — see HOW_TO_TEST.md.

Resources

• Agent Skills specification
• Anthropic — Equipping agents for the real world with Agent Skills
• vllm-project/vllm-skills — NVIDIA equivalents
• huggingface/skills — Hub workflows
• PyTorch XPU getting started
• vLLM XPU installation

License

Apache-2.0. See LICENSE.