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 agentmods add skills/huggingface/kernels/xpu-kernelsnpx skills add huggingface/kernels --skill xpu-kernelsgit clone --depth 1 https://github.com/huggingface/kernelsWhat 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 | $0.00085 | $0.04325 |
| Opus 5 | $0.00043 | $0.02162 |
| Sonnet 5 | $0.00017 | $0.00865 |
| Haiku 4.5 | $0.00009 | $0.00432 |
Grade A, and why
xpu-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 2d 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XPU Triton Kernels for Intel GPUs
This skill provides patterns and guidance for developing optimized Triton kernels targeting Intel XPU GPUs (Battlemage/Arc Pro B50). It integrates the Xe-Forge optimization framework — an LLM-driven loop that transforms PyTorch code into fast Triton kernels.
Quick Start
Optimize a Kernel (Xe-Forge Workflow)
The full optimization workflow analyzes a PyTorch baseline, generates Triton kernel variants in a branching trial tree, benchmarks each on XPU hardware, and finalizes the best result.
# 1. Analyze the baseline
python scripts/analyze_kernel.py test_kernels/70_Gemm_Sigmoid_Scaling_ResidualAdd_pytorch.py
# 2. Initialize trial tracking
python scripts/trial_manager.py init 70_Gemm_Sigmoid test_kernels/70_Gemm_Sigmoid_Scaling_ResidualAdd_pytorch.py
# 3. Validate a generated kernel (no GPU needed)
python scripts/validate_triton.py my_kernel.py
# 4. Benchmark correctness + performance
python scripts/benchmark.py test_kernels/70_Gemm_Sigmoid_Scaling_ResidualAdd_pytorch.py my_kernel.py
# 5. Profile with VTune (optional)
python scripts/xpu_profiler.py my_kernel.py
# 6. Finalize best trial
python scripts/trial_manager.py finalize 70_Gemm_Sigmoid optimized_triton.py
Supported Hardware
| GPU | Architecture | XVEs | Mem BW | Key Feature | Verified |
|---|---|---|---|---|---|
| Battlemage G21 / Arc Pro B50 | Xe2 | 128 | ~500 GB/s | Tensor descriptors, GRF 256 | Yes |
See the Intel XPU Backend for Triton for supported hardware.
When This Skill Applies
Use this skill when:
- Optimizing PyTorch operations into Triton kernels for Intel XPU
- Writing GEMM, fused kernels, reductions, or Flash Attention for Intel GPUs
- Running the Xe-Forge optimization loop (analyze → validate → benchmark → profile → finalize)
- Benchmarking kernel performance against PyTorch baseline on XPU
What ships with it
25 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.
- manifest.txt 781 B
- README.md 1.1 KB
- references/correctness.yaml 5.1 KB
- references/dtype_optimizations.yaml 4.4 KB
- references/fusion_patterns.yaml 15 KB
- references/huggingface-kernels-integration.md 10 KB
- references/implementation_reference.md 7.3 KB
- references/kernelbench-classification.md 7.1 KB
- references/memory_patterns.yaml 19 KB
- references/optimization_levels.yaml 8.2 KB
- references/optimization_strategies.md 4.3 KB
- references/persistent_kernel_patterns.yaml 8.5 KB
- references/workflow_details.md 11 KB
- references/xpu_optimizations.yaml 52 KB
- scripts/analyze_kernel.py 8.7 KB runs code
- scripts/benchmark_kernels.py 18 KB runs code
- scripts/benchmark.py 17 KB runs code
- scripts/config.py 551 B runs code
- scripts/config.yaml 279 B
- scripts/huggingface_kernels_example.py 11 KB runs code
- scripts/requirements.txt 109 B
- scripts/transformers_injection_example.py 5.8 KB runs code
- scripts/trial_manager.py 15 KB runs code
- scripts/validate_triton.py 13 KB runs code
- scripts/xpu_profiler.py 41 KB runs code
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.
- 2d ago First seen · 315 lines · 85 tokens per session scan A 6977662b08c1
xpu-kernels is a skill published in the GitHub repository huggingface/kernels (729 stars, last pushed 5d ago), licensed Apache-2.0. It adds 85 tokens to every session and 4,325 once invoked, about $0.0004 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
add-macos-statusbar
Add a macOS menu bar status indicator for NanoClaw. Shows a bolt icon with a green/red dot indicating whether NanoClaw is running, with Start, Stop, and Restart controls. macOS only.
cli-anything-3mf
3MF mesh geometry editor — detect and resize cylindrical holes, repair meshes, compare 3D printing files. Works with BambuStudio and PrusaSlicer 3MF files.
platform-port
Guide porting FastLED to new MCU platforms, including int.h types, clockless drivers, SPI implementations, and platform detection. Use when adding support for a new microcontroller family or board.
doca-compress
Use this skill for hands-on DOCA Compress programming on a BlueField DPU, ConnectX NIC, or host with DOCA — enabling compress-deflate, decompress-deflate, decompress-lz4-stream, or decompress-lz4-block tasks on a docacompress context (the hardware supports DEFLATE both directions plus LZ4 decompress; LZ4 encode is NOT…
pcbway
PCBWay PCB fabrication and assembly — turnkey/consigned assembly, design rules, ordering workflow. Alternative to JLCPCB for manufacturing. Use with KiCad. Use this skill when the user mentions PCBWay, needs turnkey assembly (PCBWay sources parts by MPN), has parts not available on LCSC, needs assembled boards with…
anta-validation
Validate Arista EOS network state with structured pass/fail verdicts using ANTA. Use for "is this switch healthy", "did my change break anything", "verify BGP/interfaces/hardware are correct", "run a health check on this device". Read-only. A test for a feature the device does not run reports notapplicable — never a…