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Official agent skill

Xpu CI Health Check

by intel in intel/torch-xpu-ops

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…

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Xpu CI Health Check

skills CLI
$ npx skills add intel/torch-xpu-ops --skill xpu-ci-health-check -a claude-code

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

GitHub CLI
$ gh skill install intel/torch-xpu-ops xpu-ci-health-check --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/xpu-ci-health-check .claude/skills/xpu-ci-health-check && 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
xpu-ci-health-check
GitHub stars
115
Token cost
~1.5k tokens
SKILL.md length
622 words
Files
2 (incl. scripts)
Skills in repo
29
Repo updated
First seen
Licence
Apache-2.0

At a glance

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…

  • Works in 3 steps: Collect evidence (always run this first) → Analyze the root cause (AI analysis… → Render output
  • The user asks to check XPU CI health
  • SKILL.md covers Prerequisites, Step 1 — Collect evidence…, Step 2 — Analyze the root… and Step 3 — Render output, plus 1 more section
  • Runs Python scripts from its folder; calls python and gh; reaches github.com and hud.pytorch.org

What it does

Xpu CI Health Check is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization. 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 with a prefilled "disable issue" link per case. USE WHEN the user asks to check XPU CI health, find failing XPU tests, or generate XPU disable-issue drafts.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/collect_failures.py`).

It sits in AI & LLM Engineering, covering Root cause analysis, Deep learning and Test generation. It works with PyTorch. The licence is Apache-2.0.

When your agent uses it

  • The user asks to check XPU CI health
  • Find failing XPU tests
  • Generate XPU disable-issue drafts

Example prompts

  • “disable issue”
  • “/xpu-ci-health-check”

Requirements

  • Python 3

Workflow steps

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

  1. Collect evidence (always run this first)
  2. Analyze the root cause (AI analysis required)
  3. Render output

What it can do on your machine

Read from SKILL.md and the folder at commit d8bc81a. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • gh

    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
    • hud.pytorch.org

    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

Xpu CI Health Check loads about 1.5k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 622 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from intel/torch-xpu-ops at commit d8bc81a, republished under its Apache-2.0 licence (© intel). 622 words, ~1,519 tokens.

Download SKILL.mdSave it as .claude/skills/xpu-ci-health-check/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
xpu-ci-health-check
description
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 with a prefilled "disable issue" link per case. USE WHEN the user asks to check XPU CI health, find failing XPU tests, or generate XPU disable-issue drafts.

XPU CI Health Check Skill

This skill automates XPU CI health checks with the following workflow:

  1. Collect failure evidence automatically by running the bundled script.
  2. Analyze the root cause of each case yourself (AI), using the traceback evidence plus the suspect commit/PR — never copy the raw exception as the conclusion.
  3. Render the result generate a list with one row per failing case.

Root cause analysis is YOUR job, not the script's. The script only gathers evidence (case id, traceback excerpt, commit sha, disable link). You must reason about why it failed and which change likely caused it.

Prerequisites

  • GitHub token: Optional. The script uses the gh CLI if --token is not provided; ensure gh auth login is configured.

Step 1 — Collect evidence (always run this first)

Run the bundled collection script from the PyTorch repository root. The script is located at <path/to/skills>/xpu-ci-health-check/scripts/collect_failures.py (included in this skill).

bash
# Run from PyTorch repository root
cd /path/to/pytorch  # or your PyTorch clone location
python </path/to/skills>/xpu-ci-health-check/scripts/collect_failures.py --run-limit 1
Flags
  • --run-limit N — Inspect the last N completed main runs (default: 1).
  • --token — GitHub API token. If omitted, the script falls back to gh CLI auth.

The script prints a JSON evidence bundle to stdout:

jsonc
{
  "runs": [{ "run_number": 9275, "head_sha": "5ef6fae…", "html_url": "…" }],
  "cases": [
    {
      "case_id": "test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_xxx",
      "commit_sha": "5ef6fae…",
      "commit_short": "5ef6fae",
      "hud_url": "https://hud.pytorch.org/pytorch/pytorch/commit/5ef6fae…",
      "commit_url": "https://github.com/pytorch/pytorch/commit/5ef6fae…",
      "job_name": "linux-jammy-xpu-… / test (default, …)",
      "failure_line": "… FAILED …",
      "error_excerpt": "…traceback / exception text…",
      "issue_title": "DISABLED test_xxx (__main__.TestCutlassBackend)",
      "issue_body": "Platforms: xpu\n\nThis test was disabled because…\n\ncc …",
      "issue_labels": ["module: xpu", "triaged"],
      "issue_url": "https://github.com/pytorch/pytorch/issues/new?title=DISABLED%20…&body=…&labels=module%3A%20xpu,triaged"
    }
  ]
}

Disable issue template is frozen in the script. The issue_title, issue_body, issue_labels, and issue_url fields are produced by build_disable_issue() inside scripts/collect_failures.py, aligned with the reference issue template https://github.com/pytorch/pytorch/issues/185907 (Platforms line + "recent examples" section + cc mention + labels module: xpu, triaged). When creating an issue, use these exact fields verbatim — never rewrite the title, body, or labels.

Step 2 — Analyze the root cause (AI analysis required)

If the number of failed cases > 10, skip this step and let the root cause field be empty. For each case in cases, use subagent to do:

  1. Fetch latest origin main and git checkout to the commit_sha locally.

  2. Identify the real failure: Read error_excerpt and identify the failing frame and exception type (e.g., InductorError: NotImplementedError: ), not just the surface FAILED line.

  3. Root cause:

    • Determine regression status of the case:
      • Newly added or updated case → No
      • Existing old case (appeared before) → Yes
      • Insufficient evidence → Unknown (explain why)
    • Find the code change that cause the case failure and also the guilty commit/PR.
    • Analysis the root cause.
    • Write root cause in a strict 3-point structure (do not omit any point):
      • Introduced by which PR: Identify the most likely PR/commit that introduced the failure. If uncertain, state the top suspect and what evidence is missing.
      • Root cause of the failure: Explain the concrete failing mechanism on XPU using evidence from traceback/logs.
      • Is this fail only on XPU: try to analysis if cuda will fails, if no evidence is available, state that explicitly.
    • Use available history (recent runs, torch-ci failure history, prior reports).
Show full SKILL.md (183 more words)Show less

Step 3 — Render output

Produce a list these details, one row per case:

List the details for each case as follows:
  • Commit: `<commit_short>` linked to HUD (e.g., [5ef6fae](https://hud.pytorch.org/...)).
  • Case: The case_id from the evidence bundle.
  • Is regression: Yes / No / Unknown (see Step 2.4; No = newly appeared).
  • Root cause: Both required points from Step 2 (introduced PR + why XPU fails mechanism).
  • Disable link: [Create disable issue](<issue_url>).
    • IMPORTANT: Use issue_url exactly as produced by the script — do not truncate.
    • The full URL carries prefilled title, body (Platforms + recent examples + cc), and labels (module: xpu, triaged).
    • A URL with only ?title=... is invalid and will not match the template.

After the list, add a one-line health summary:

  • Whether main is green or red for ciflow/xpu.
  • The run number(s) and count of failing cases inspected.

Important Notes

  • Zero failures: If the script returns no cases, report that main is green for the inspected run(s). Do not fabricate failures.
  • No auto-creation: Disable links are drafts only. A human must review and approve before creating issues.
  • Template reference: The frozen template follows https://github.com/pytorch/pytorch/issues/185907 exactly (Platforms + recent examples + cc + labels).

© intel, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in .claude/skills/xpu-ci-health-check of intel/torch-xpu-ops.

  • SKILL.md
  • scripts/collect_failures.py

Open the folder on GitHubat commit d8bc81a

Compare with similar skills

Xpu CI Health Check 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.

Xpu CI Health Check compared with similar skills
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Ascendcascend-ai-coding/awesome-ascend-skills174—~3.5kAutomated safety check: PassNone
Vllm Pytorch CI Triagepytorch/test-infra113—~2.4kAutomated safety check: PassCustom licence
Docstringpytorch/pytorch104k2 repos~2.6kAutomated safety check: PassCustom licence
ExecuTorch Cortex-M Backendpytorch/executorch5.1k—~872Automated safety check: PassCustom licence

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Works with

Categories

Questions about Xpu CI Health Check

What does Xpu CI Health Check do?

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…. Xpu CI Health Check is an agent skill from intel/torch-xpu-ops, published by the product's own GitHub organization.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 with a prefilled "disable issue" link per case.

When should I use Xpu CI Health Check?

Xpu CI Health Check fits situations like: the user asks to check XPU CI health; find failing XPU tests; generate XPU disable-issue drafts.

How do I install Xpu CI Health Check in Claude Code?

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

How do I install Xpu CI Health Check in Codex?

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

Can I use Xpu CI Health Check 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 xpu-ci-health-check -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/xpu-ci-health-check, .gemini/skills/xpu-ci-health-check, .github/skills/xpu-ci-health-check and .opencode/skills/xpu-ci-health-check in your project.

What does Xpu CI Health Check need to run?

Going by SKILL.md and its folder, Xpu CI Health Check needs Python for the scripts in its folder and the command-line tools its instructions call (python and gh). Our summary lists: Python 3.

Does Xpu CI Health Check access the network?

SKILL.md names 2 domains. In commands or code: github.com and hud.pytorch.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Xpu CI Health Check 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Xpu CI Health Check use?

Xpu CI Health Check 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 Xpu CI Health Check use?

About 1.5k tokens (SKILL.md is roughly 6.1k 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 Xpu CI Health Check?

Skills that share tags, products or a category with Xpu CI Health Check: The Art of Debugging (stas00/the-art-of-debugging, 1.7k stars), Ascendc (ascend-ai-coding/awesome-ascend-skills, 174 stars), Vllm Pytorch CI Triage (pytorch/test-infra, 113 stars) and Docstring (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 Xpu CI Health Check?

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 10, 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.