New: 97,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Official agent skill

Setup

by intel in intel/torch-xpu-ops

Set up Intel GPU unitrace profiling tool. An agent skill from intel/torch-xpu-ops.

OfficialMITAuto-check passed

Install Setup

skills CLI
$ npx skills add intel/torch-xpu-ops --skill setup -a claude-code

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

GitHub CLI
$ gh skill install intel/torch-xpu-ops setup --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/action/unitrace/setup .claude/skills/setup && 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/

Facts

Skill name
setup
GitHub stars
115
Token cost
~566 tokens
SKILL.md length
113 words
Files
1
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

Set up Intel GPU unitrace profiling tool. An agent skill from intel/torch-xpu-ops.

  • Works in 5 steps: Check if unitrace is already available → Check prerequisites → Clone and build → …
  • The user mentions unitrace
  • Calls cmake, python3 and git; reaches github.com
  • Intel GPU tracing

What it does

Setup is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. Set up Intel GPU unitrace profiling tool. Use this skill whenever the user mentions unitrace, Intel GPU tracing, pti-gpu tracing tool, GPU profiling with unitrace, or wants to build/install unitrace from source. Also trigger when the user asks about tracing Intel GPU workloads with unitrace, profiling SYCL/Level Zero/OpenCL applications on Intel GPUs using unitrace, or setting up pti-gpu tools. This skill handles checking if unitrace is already available, and if not, cloning and building it from source.

Its SKILL.md is about 570 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is MIT.

When your agent uses it

  • The user mentions unitrace
  • Intel GPU tracing
  • Pti-gpu tracing tool
  • GPU profiling with unitrace

Example prompts

  • “/setup”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Check if unitrace is already available
  2. Check prerequisites
  3. Clone and build
  4. Add to PATH
  5. Verify

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

    Shell commands in SKILL.md call:

    • cmake
    • python3
    • git
    • make

    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

Setup loads about 566 tokens when it runs. Until then it costs about 129 tokens; SKILL.md has 113 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~129
When it runs · the whole SKILL.md, loaded when a task matches
~566

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 MIT licence (© intel). 113 words, ~566 tokens.

Download SKILL.mdSave it as .claude/skills/setup/SKILL.md (or your agent's skills folder).
name
setup
description
Set up Intel GPU unitrace profiling tool. Use this skill whenever the user mentions unitrace, Intel GPU tracing, pti-gpu tracing tool, GPU profiling with unitrace, or wants to build/install unitrace from source. Also trigger when the user asks about tracing Intel GPU workloads with unitrace, profiling SYCL/Level Zero/OpenCL applications on Intel GPUs using unitrace, or setting up pti-gpu tools. This skill handles checking if unitrace is already available, and if not, cloning and building it from source.
license
MIT
metadata.unitrace
Intel PTI-GPU
metadata.oneAPI
Intel oneAPI
metadata.XPU
Intel GPU
metadata.LevelZero
Intel Level Zero

Intel unitrace Setup

Set up unitrace from Intel PTI-GPU. Always check PATH first before building.

Instructions

Step 1: Check if unitrace is already available
bash
which unitrace 2>/dev/null && unitrace --help > /dev/null 2>&1 && echo "UNITRACE_AVAILABLE" || echo "UNITRACE_NOT_FOUND"
  • If found: Report the path. Use it directly unless the user explicitly asks to rebuild.
  • If NOT found: Proceed to Step 2.
Step 2: Check prerequisites
bash
which g++ 2>/dev/null || which icpx 2>/dev/null || echo "NO_CXX_COMPILER"
cmake --version 2>/dev/null || echo "NO_CMAKE"
echo "CMPLR_ROOT=${CMPLR_ROOT:-NOT_SET}"
python3 --version 2>/dev/null || echo "NO_PYTHON"

Required:

  • CMake 3.22+
  • C++17 compiler (g++ or icpx)
  • Intel oneAPI (use the "source-oneapi" skill if not initialized)
  • Python 3.9+
Step 3: Clone and build

Default location: $HOME/.local/src/pti-gpu.

bash
mkdir -p "$HOME/.local/src"
cd "$HOME/.local/src"
git clone https://github.com/intel/pti-gpu.git
cd pti-gpu/tools/unitrace
mkdir -p build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j$(nproc)

If MPI is not available, add -DBUILD_WITH_MPI=0.

Build options:

OptionDefaultDescription
BUILD_WITH_MPI1MPI profiling
BUILD_WITH_ITT1oneCCL/oneDNN profiling
BUILD_WITH_XPTI1SYCL/UR profiling
BUILD_WITH_OPENCL1OpenCL profiling
Step 4: Add to PATH
bash
export PATH="$HOME/.local/src/pti-gpu/tools/unitrace/build:$PATH"
Step 5: Verify
bash
unitrace --help

© intel, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/action/unitrace/setup of intel/torch-xpu-ops.

Open the folder on GitHubat commit 0187b3b

Compare with similar skills

Setup 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.

Setup compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Setup this skillintel/torch-xpu-ops115—~566Automated safety check: PassMIT
Optimize For GPUK-Dense-AI/scientific-agent-skills48k1 repos~3.4kAutomated safety check: PassMIT
Profileccusage/ccusage19k—~430Automated safety check: PassCustom licence
GPU Kubernetes Operationssickn33/agentic-awesome-skills47k2 repos~3.2kAutomated safety check: PassMIT
Codex Profilessickn33/agentic-awesome-skills47k1 repos~1.7kAutomated safety check: PassMIT
Collectors Snmp Profilesnetdata/netdata81k—~3.1kAutomated safety check: PassGPL-3.0

Similar skills

  • Optimize For GPU

    K-Dense-AI/scientific-agent-skills

    GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster.

    48k GitHub starsUsed in 1 repo~3.4k tokens
    Data & AnalyticsAuto-check passed

  • Profile

    ccusage/ccusage

    Profiles ccusage performance. An agent skill from ccusage/ccusage.

    19k GitHub stars~430 tokensUpdated today
    Auto-check passed

  • GPU Kubernetes Operations

    sickn33/agentic-awesome-skills

    Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.

    47k GitHub starsUsed in 2 repos~3.2k tokens
    DevOps & CloudAuto-check passed

  • Codex Profiles

    sickn33/agentic-awesome-skills

    Use codex-profiles to run Codex CLI or Codex Desktop with isolated CODEXHOME profiles for separate accounts, projects, and local state.

    47k GitHub starsUsed in 1 repo~1.7k tokens
    Auto-check passed

  • Collectors Snmp Profiles

    netdata/netdata

    Author or review Netdata SNMP polling profiles, ddsnmp parsing, topology/licensing/BGP rows and profile-format documentation.

    81k GitHub stars~3.1k tokensUpdated today
    Frontend & DesignAuto-check passed

  • Competitor Profiling

    sickn33/agentic-awesome-skills

    When the user wants to research, profile, or analyze competitors from their URLs.

    47k GitHub starsUsed in 1 repo~3.6k tokens
    Marketing & SEOAuto-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

Questions about Setup

What does Setup do?

Set up Intel GPU unitrace profiling tool. An agent skill from intel/torch-xpu-ops. Setup is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. Set up Intel GPU unitrace profiling tool.

When should I use Setup?

Setup fits situations like: the user mentions unitrace; intel GPU tracing; pti-gpu tracing tool; GPU profiling with unitrace.

How do I install Setup in Claude Code?

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

How do I install Setup in Codex?

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

Can I use Setup 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 setup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/setup, .gemini/skills/setup, .github/skills/setup and .opencode/skills/setup in your project.

What does Setup need to run?

Going by SKILL.md and its folder, Setup needs the command-line tools its instructions call (cmake, python3, git and make). Our summary lists: Python 3.

Does Setup 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 Setup 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 Setup use?

Setup is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Setup use?

About 566 tokens (SKILL.md is roughly 2.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Setup?

Skills that share tags, products or a category with Setup: Optimize For GPU (K-Dense-AI/scientific-agent-skills, 48k stars), Profile (ccusage/ccusage, 19k stars), GPU Kubernetes Operations (sickn33/agentic-awesome-skills, 47k stars) and Codex Profiles (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Setup?

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.