Development guidance for writing and testing C++ code that runs mathematical operations on x86 CPUs using AVX2 and AVX512 instructions.

Writing, building, benchmarking, profiling, and selecting CPU versions of Hugging Face kernels for Intel Xeon and compatible x86 processors.

It gives coding agents a structured way to check that a kernel is correct before exploring speed improvements, reducing guesswork when optimizing CPU code.

cpu-kernels — Skill for Claude Code by huggingface/kernels

About the project

Hugging Face Kernels is a system that lets Python libraries and applications load compute kernels directly from the Hugging Face Hub. It is used by Python and PyTorch developers who need portable kernels that can support multiple versions and build configurations. The catalogue skills help agents work with the kernels system and its kernel-building tools.

huggingface/kernels · 774 stars · on GitHub · huggingface.co

Install

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.

Any agent

npx skills add huggingface/kernels --skill cpu-kernels

Clone the repo

git clone --depth 1 https://github.com/huggingface/kernels

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/huggingface/kernels/cpu-kernels/github.svg)](https://agentmods.dev/skills/huggingface/kernels/cpu-kernels)

Your own site

<a href="https://agentmods.dev/skills/huggingface/kernels/cpu-kernels"><img src="https://agentmods.dev/badge/skills/huggingface/kernels/cpu-kernels/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/huggingface/kernels/cpu-kernels"><img src="https://agentmods.dev/badge/skills/huggingface/kernels/cpu-kernels.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 6,865 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00089	$0.06865
Opus 5.5	$0.00036	$0.02746
Sonnet 5.5	$0.00018	$0.01373
Haiku 4.5	$0.00009	$0.00686

Measured 1mo ago against content hash b7686f78f8b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cpu-kernels 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 1mo ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/analyze_op.py, scripts/benchmark_cpu.py, scripts/config.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

kernel-builder/skills/cpu-kernels/SKILL.md · 462 lines

How it starts

The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CPU C++ Kernels for x86 Processors

This skill provides patterns and guidance for developing optimized C++ kernels targeting x86 CPUs (Intel Xeon and compatible processors) with AVX2 and AVX512 intrinsics. Kernels are compiled via kernel-builder and distributed through the Hugging Face kernels ecosystem.

Who runs these commands? You, the agent — not a human. This is an autonomous loop: you write/edit the C++ kernel, build it, then run the scripts below as tools (via Bash) to check correctness, benchmark, and profile. You read each result, record it with trial_manager.py, decide the next change from the Phase 2 decision tree, and repeat until you hit early_stop_speedup or run all max_trials.

Key Concepts (read before the Quick Start)

The commands use a few names that mean different things. They are not interchangeable:

Name (example)	What it is	Used by
`baseline.py`	The PyTorch reference implementation you optimize against. It is the ground truth for correctness and the speed reference for speedup. It must define `get_inputs()` and either `get_reference_output()` or a `Model` class (plus optional `get_init_inputs()`). You write this file (or it is given) before starting.	every script
`my_rmsnorm`	A trial-tree label — an arbitrary name you pick for this optimization task. `trial_manager.py` stores all attempts under `trials/my_rmsnorm/`. It is only a tracking ID.	`trial_manager.py` only
`my_kernel`	The installed Python package name — the build artifact produced by `kernel-builder build` + `pip install`. This is the importable module that contains your compiled kernel.	`--kernel-package`
`my_kernel.rms_norm`	An `<package>.<function>` path — the actual callable inside the installed package. Passed to `--op` to tell the benchmark/profiler which function to run.	`--op`

⚠️ --op means two different things depending on the script. In analyze_op.py, --op is a plain operation name (e.g. "rms_norm") used to look up compute/memory characteristics. In benchmark_cpu.py and cpu_profiler.py, --op is a package.function path (e.g. my_kernel.rms_norm) used to import and call your kernel. Same flag, different meaning — read each command below carefully.

Read the full file on GitHub · 462 lines

Files

What ships with it

22 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.

Changes

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.

1mo ago First seen · 462 lines · 89 tokens per session scan A b7686f78f8b9

Subscribe to this mod's changes

cpu-kernels is a skill published in the GitHub repository huggingface/kernels (774 stars, last pushed today), licensed Apache-2.0. It adds 89 tokens to every session and 6,865 once invoked, about $0.0004 per session on Opus 5.5. 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.

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