Official agent skill

Ut Refactor Review

Review PyTorch upstream unit-test (UT) PRs that enable Intel GPU (XPU) on existing tests.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Ut Refactor Review

skills CLI

$ npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops ut-refactor-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/ut-refactor-review .claude/skills/ut-refactor-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "ut-refactor-review" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review into .claude/skills/ut-refactor-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ut-refactor-review", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops ut-refactor-review --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/ut-refactor-review .agents/skills/ut-refactor-review && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "ut-refactor-review" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review into .agents/skills/ut-refactor-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ut-refactor-review", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops ut-refactor-review --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/ut-refactor-review .cursor/skills/ut-refactor-review && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "ut-refactor-review" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review into .cursor/skills/ut-refactor-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ut-refactor-review", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/intel/torch-xpu-ops.git --path .claude/skills/ut-refactor-review

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops ut-refactor-review --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/ut-refactor-review .gemini/skills/ut-refactor-review && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "ut-refactor-review" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review into .gemini/skills/ut-refactor-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ut-refactor-review", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install intel/torch-xpu-ops ut-refactor-review

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/ut-refactor-review .github/skills/ut-refactor-review && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "ut-refactor-review" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review into .github/skills/ut-refactor-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ut-refactor-review", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install intel/torch-xpu-ops ut-refactor-review --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/intel/torch-xpu-ops.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/ut-refactor-review .opencode/skills/ut-refactor-review && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "ut-refactor-review" agent skill from https://github.com/intel/torch-xpu-ops/tree/main/.claude/skills/ut-refactor-review into .opencode/skills/ut-refactor-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ut-refactor-review", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: ut-refactor-review
GitHub stars: 115
Token cost: ~917 tokens
SKILL.md length: 319 words
Files: 2 (incl. references)
Skills in repo: 29
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Review PyTorch upstream unit-test (UT) PRs that enable Intel GPU (XPU) on existing tests.

Reviewing PRs under test/ that port device-generic tests to XPU
SKILL.md covers Scope: when this skill applies, Usage Modes, Review Philosophy and Files to Reference
Reaches github.com
Add allowxpu=True

What it does

Ut Refactor Review is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. Review PyTorch upstream unit-test (UT) PRs that enable Intel GPU (XPU) on existing tests. Use when reviewing PRs under test/ that port device-generic tests to XPU, add allowxpu=True, generalize CUDA-hardcoded tests, or add XPU skips/xfails/tolerance overrides in OpInfo.

Its SKILL.md is about 920 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/xpu-ut-review-checklist.md`).

It sits in AI & LLM Engineering, covering Deep learning, Refactoring and Unit testing. It works with PyTorch and CUDA. The licence is Apache-2.0.

When your agent uses it

Reviewing PRs under test/ that port device-generic tests to XPU
Add allowxpu=True
Generalize CUDA-hardcoded tests
Add XPU skips/xfails/tolerance overrides in OpInfo

Example prompts

“/ut-refactor-review”

What it can do on your machine

Read from SKILL.md and the folder at commit 0187b3b. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ut Refactor Review loads about 917 tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 319 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~73
When it runs · the whole SKILL.md, loaded when a task matches: ~917
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from intel/torch-xpu-ops at commit 0187b3b, republished under its Apache-2.0 licence (© intel). 319 words, ~917 tokens.

Download SKILL.mdSave it as .claude/skills/ut-refactor-review/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: ut-refactor-review
description: Review PyTorch upstream unit-test (UT) PRs that enable Intel GPU (XPU) on existing tests. Use when reviewing PRs under test/ that port device-generic tests to XPU, add allow_xpu=True, generalize CUDA-hardcoded tests, or add XPU skips/xfails/tolerance overrides in OpInfo.

XPU UT Refactor Review Skill

Review PyTorch (pytorch/pytorch) pull requests that enable XPU on existing upstream unit tests. These PRs almost never add new operator logic; they make existing tests device-agnostic and opt XPU into them. The review must focus on what CI cannot check: whether the generalization preserves the original test intent, whether device gating is precise, and whether every skip/xfail is justified and traceable.

Scope: when this skill applies

Use this skill (instead of the generic pr-review skill in pytorch/pytorch) when the diff is predominantly:

test/** changes that swap CUDA-hardcoded constructs for device-generic ones
instantiate_device_type_tests(..., allow_xpu=True) additions
onlyAccelerator / onlyNativeDeviceTypesAnd([...]) decorator migrations
XPU entries in OpInfo (common_methods_invocations.py, opinfo/definitions/*): DecorateInfo(... device_type='xpu' ...), toleranceOverride, skips, xfails
New TestXxxDevice classes split out from a device-agnostic TestXxx

If the PR also changes operator kernels or native_functions.yaml, hand those files to the pr-review skill and apply this skill only to the test files.

Usage Modes

No Argument

If invoked with no arguments, do not review. Ask:

What would you like me to review?

A PR number or URL (e.g., 159118 or the full PR URL)

A local branch

PR Mode

/ut-refactor-review 159118
/ut-refactor-review https://github.com/pytorch/pytorch/pull/159118
/ut-refactor-review 159118 detailed

Obtain the PR title, description, diff, changed-file list, and existing review comments before reviewing. If the command does not name a repo, default to fetching the PR from pytorch/pytorch.

Suggested fetch commands (CLI environments with gh):

gh pr view <PR_NUMBER> --repo pytorch/pytorch --json title,body,author,baseRefName,headRefName,files,additions,deletions,commits
gh pr diff <PR_NUMBER> --repo pytorch/pytorch
gh pr view <PR_NUMBER> --repo pytorch/pytorch --json comments,reviews

Local Branch Mode

/ut-refactor-review branch
/ut-refactor-review branch detailed

Review the current branch's changes relative to main (diff, commit log, and changed-file list). Use the branch name in the review header instead of a PR number.

Review Philosophy

Go through every changed line against references/xpu-ut-review-checklist.md. For anything this skill does not address, defer to the pr-review skill in pytorch/pytorch.

Files to Reference

references/xpu-ut-review-checklist.md — the line-by-line checklist
torch/testing/_internal/common_device_type.py — instantiate_device_type_tests, onlyAccelerator, allow_xpu, only_for
torch/testing/_internal/common_utils.py — TEST_XPU, TEST_CUDA, TEST_HPU, xfailIf, HardwareClassification
torch/testing/_internal/common_methods_invocations.py, torch/testing/_internal/opinfo/definitions/* — OpInfo DecorateInfo, toleranceOverride

Files

SKILL.md and 1 other file (references) in .claude/skills/ut-refactor-review of intel/torch-xpu-ops.

SKILL.md
references/xpu-ut-review-checklist.md

Open the folder on GitHubat commit 0187b3b

Compare with similar skills

Ut Refactor Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ut Refactor Review compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Ut Refactor Review this skillintel/torch-xpu-ops	115	—	~917	Automated safety check: Pass	Apache-2.0	yesterday
Cuda Index Widthpytorch/pytorch	104k	—	~1.6k	Automated safety check: Pass	Custom licence	today
Liger Kernel Devlinkedin/Liger-Kernel	6.6k	—	~799	Automated safety check: Pass	BSD-2-Clause	today
Graphsignalgraphsignal/graphsignal	257	—	~6.2k	Automated safety check: Pass	Apache-2.0	9 days ago
Metal Kernelpytorch/pytorch	104k	—	~4.9k	Automated safety check: Pass	Custom licence	today
Validating Pytorch Custom Opsmeta-pytorch/attention-gym	1.3k	—	~6.4k	Automated safety check: Pass	BSD-3-Clause	yesterday

Similar skills

Cuda Index Width
pytorch/pytorch
Choose 32-bit vs 64-bit index math in PyTorch CUDA kernels. An agent skill from pytorch/pytorch.
104k GitHub stars~1.6k tokensUpdated today
AI & LLM EngineeringAuto-check passed
Liger Kernel Dev
linkedin/Liger-Kernel
Develops production-ready Triton kernels for Liger Kernel. An agent skill from linkedin/Liger-Kernel.
6.6k GitHub stars~799 tokensUpdated today
AI & LLM EngineeringAuto-check passed
Graphsignal
graphsignal/graphsignal
Profile AI inference workloads (vLLM, SGLang, TensorRT-LLM, PyTorch, any GPU application) with the Graphsignal profiler and read the results from its local /signals JSON endpoint.
257 GitHub stars~6.2k tokensUpdated 9 days ago
AI & LLM EngineeringAuto-check passed
Metal Kernel
pytorch/pytorch
Write Metal/MPS kernels for PyTorch operators. An agent skill from pytorch/pytorch.
104k GitHub stars~4.9k tokensUpdated today
AI & LLM EngineeringAuto-check passed
Validating Pytorch Custom Ops
meta-pytorch/attention-gym
Ensures new Attention Gym eager, Triton, CuTeDSL, and external-library implementations are torch.compile-friendly and correctly registered.
1.3k GitHub stars~6.4k tokensUpdated yesterday
AI & LLM EngineeringAuto-check passed
Ako4all
TongmingLAIC/AKO4ALL
Drive an agentic loop that iteratively optimizes a GPU kernel for maximum speedup.
369 GitHub stars~4k tokensUpdated 22 days ago
AI & LLM EngineeringAuto-check passed

More from intel/torch-xpu-ops

All 29 skills in this repo

Intel GPU Device Selection
intel/torch-xpu-ops
Official
Select the Intel GPU device to use when a system has multiple Intel GPU devices.
115 GitHub stars~508 tokensUpdated yesterday
Auto-check passed
Xpu CI Health Check
intel/torch-xpu-ops
Official
Check PyTorch ciflow/xpu (xpu.yml) on the main branch, collect the failing XPU test cases from the most recent completed run(s), analyze the ROOT CAUSE of each failure with AI, and produce a list…
115 GitHub stars~1.5k tokensUpdated yesterday
Auto-check passed
At Dispatch V2
intel/torch-xpu-ops
Official
Convert PyTorch ATDISPATCH macros to ATDISPATCHV2 format in ATen C++ code.
115 GitHub starsUsed in 3 repos~2.2k tokens
Auto-check passed
PR Review
intel/torch-xpu-ops
Official
Review pull requests for XPU operator or backend code. An agent skill from intel/torch-xpu-ops.
115 GitHub stars~4.2k tokensUpdated yesterday
Auto-check passed
Skill Writer
intel/torch-xpu-ops
Official
Guide users through creating Agent Skills for Claude Code. An agent skill from intel/torch-xpu-ops.
115 GitHub starsUsed in 3 repos~2.4k tokens
Auto-check passed
Ut Issue Authoring
intel/torch-xpu-ops
Official
Read the evidence a nightly UT run produced, decide which failures share a root cause and which are machine breakage rather than product bugs, and write one issue draft per root cause to drafts.json.
115 GitHub stars~2k tokensUpdated yesterday
Auto-check passed

Works with

Questions about Ut Refactor Review

What does Ut Refactor Review do?

Review PyTorch upstream unit-test (UT) PRs that enable Intel GPU (XPU) on existing tests. Ut Refactor Review is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. Review PyTorch upstream unit-test (UT) PRs that enable Intel GPU (XPU) on existing tests.

When should I use Ut Refactor Review?

Ut Refactor Review fits situations like: reviewing PRs under test/ that port device-generic tests to XPU; add allowxpu=True; generalize CUDA-hardcoded tests; add XPU skips/xfails/tolerance overrides in OpInfo.

How do I install Ut Refactor Review in Claude Code?

Run `npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a claude-code`. Or copy the skill folder (.claude/skills/ut-refactor-review in intel/torch-xpu-ops) into .claude/skills/ut-refactor-review in your project. Claude Code loads it when a task matches its description.

How do I install Ut Refactor Review in Codex?

Run `npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a codex`. Or copy the skill folder (.claude/skills/ut-refactor-review in intel/torch-xpu-ops) into .agents/skills/ut-refactor-review in your project. Codex loads it when a task matches its description.

Can I use Ut Refactor Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add intel/torch-xpu-ops --skill ut-refactor-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ut-refactor-review, .gemini/skills/ut-refactor-review, .github/skills/ut-refactor-review and .opencode/skills/ut-refactor-review in your project.

What does Ut Refactor Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Ut Refactor Review is instructions for the agent only.

Does Ut Refactor Review access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Ut Refactor Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Ut Refactor Review use?

Ut Refactor Review is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ut Refactor Review use?

About 917 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.

What are the alternatives to Ut Refactor Review?

Skills that share tags, products or a category with Ut Refactor Review: Cuda Index Width (pytorch/pytorch, 104k stars), Liger Kernel Dev (linkedin/Liger-Kernel, 6.6k stars), Graphsignal (graphsignal/graphsignal, 257 stars) and Metal Kernel (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ut Refactor Review?

intel (a GitHub organization, an official publisher) maintains it in intel/torch-xpu-ops, which has 115 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 6, 2026.

Source: intel/torch-xpu-ops on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Ut Refactor Review

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

XPU UT Refactor Review Skill

Scope: when this skill applies

Usage Modes

No Argument

PR Mode

Local Branch Mode

Review Philosophy

Files to Reference

Files

Compare with similar skills

Similar skills

More from intel/torch-xpu-ops

Related

Questions about Ut Refactor Review