Retrieval is the whole mechanism, so be precise about what it does. A skill is a procedure the agent pulls into context when the task matches its description, and the installer copies those files into the directories each supported agent already reads [8]. Nothing about the hardware changes. What changes is whether the model in your terminal knows that llama.cpp on Intel means the SYCL backend before you do [6]. According to runtimewire, that is the competitive point: GPU competition increasingly turns on whether a developer can operate the hardware without memorizing a second vendor stack [25].
Nine of the twenty skills are host prep and model launch [27]. Four inspect drivers, permissions, Docker access and shared memory [5]. Five start models on PyTorch XPU, vLLM-XPU, SGLang-XPU or llama.cpp's SYCL backend [6]. The other eleven are the judgment calls: memory estimates, configuration recommendations, benchmarks, profiles, and CUDA migration assessments that can also execute the migration [7].
The sizing skill is where I would start, because it makes an arithmetic claim you can audit. Intel's example assigns 16.8 GiB to weights and 3.2 GiB to key-value cache for a quantized Qwen2.5-32B at 8K context and four concurrent requests, then reports 11.2 GiB of headroom on a 32 GiB Arc Pro B70 [11]. Those two line items sum to 20 GiB, or 62.5 percent of the card [13]. Subtract 20 from 32 and you get 12, not 11.2, so 0.8 GiB is held back by something the published breakdown does not name [14]. That gap is the useful part. It implies the calculator carries an overhead term, and an overhead term is exactly what fails to transfer when the workload changes.
For the headroom figure to mean anything on your card, the quantization, the KV dtype, the context length and the concurrency all have to match the example [11]. The 3.2 GiB cache number is 8K times four requests. Serve 16K, or eight requests, and the calculation that left 11.2 GiB spare is describing a different machine.
The serving check is the piece of craft I would keep. Intel says vllm-xpu-run picks a container, supplies the device flags, starts an OpenAI-compatible endpoint, and then confirms that the endpoint returns a completion [15]. A process that is up and a model that answers are different states, and a probe that only proves the first has fooled better engineers than me.
The verification numbers want reading as the authors' own harness. They report 260 executable-contract checks, with 231 passing, 29 producing advisory warnings and none failing [17]. Roughly 11 percent of checks therefore land in a category the authors defined the threshold for [18]. They also report end-to-end testing on physical Arc Pro B70 and Arc B580 hardware [19], and the repository ships unit tests for the memory calculator plus static validation scripts for skill changes [20]. That is decent engineering hygiene, and it is still a claim about their bench, not your fleet.
Which brings me to the config line. The security policy states that sglang-xpu-run and llamacpp-xpu-run use --host 0.0.0.0, while vllm-xpu-run publishes Docker port 8000 on all host interfaces [22]. The same documentation recommends binding to 127.0.0.1, fronting the service with an authenticating reverse proxy, or restricting access [24]. When an agent can issue these commands without a human at the keyboard, the operative default is the flag in the file, not the advice in the README. On a single-user workstation I would install these today. On a shared lab subnet I would patch the host binding first, because the skill will not ask.
