xpu-ci-health-check

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).

# 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:

{
  "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…"
       
       
        
       
    
  

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).

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).