菜单
Generate and analyze T1/T2/R roofline reports for PyTorch OOB workloads comparing Intel XPU and NVIDIA CUDA. Use when working with eager profiling artifacts, per-model reports, fleet summaries, graph consistency, or XPU-vs-CUDA software efficiency analysis.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/intel/torch-xpu-ops --skill oob-perf-analysis命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
基于 SOC 职业分类
| name | oob-perf-analysis |
| description | Generate and analyze T1/T2/R roofline reports for PyTorch OOB workloads comparing Intel XPU and NVIDIA CUDA. Use when working with eager profiling artifacts, per-model reports, fleet summaries, graph consistency, or XPU-vs-CUDA software efficiency analysis. |
Analyzes OOB eager-mode performance using T1/T2/R roofline methodology to compare XPU and CUDA software efficiency.
Core outputs (all under agent_space_xpu/, git-ignored):
agent_space_xpu/reports/<session>/models/agent_space_xpu/reports/<session>/summary_eager_inference.mdagent_space_xpu/reports/<session>/graph_consistency_eager_inference.mdagent_space_xpu/reports/<session>/insights_summary.md| File | Purpose |
|---|---|
| methodology.md | T1/T2/R definitions, formulas, classification thresholds |
| inputs.md | Input file formats, completeness rules, output paths |
| per-model-report.md | Per-model analysis steps and 5-section report structure |
| fleet-summary.md | Fleet aggregation steps and 7-section report structure |
| graph-consistency.md | Graph consistency analysis |
| insights.md | Developer-facing insights summary |
| troubleshooting.md | Diagnosing abnormal R, trace issues, and data problems |
User has raw artifacts and wants model-level T1/T2/R analysis.
Follow: methodology.md → inputs.md → per-model-report.md
User wants fleet-wide comparison, model scorecards, op ranking, or projection-quality aggregation.
Follow: fleet-summary.md (references graph-consistency.md for Section 7)
User wants to compare CUDA vs XPU computational graphs.
Follow: graph-consistency.md
Per-model reports and fleet summary exist; user wants a concise developer-facing summary.
Follow: insights.md
User asks why R is abnormal, why traces disagree, or why results look suspicious.
Follow: troubleshooting.md
methodology.md before computing any metric.inputs.md before accessing any artifact.If a calling workflow explicitly requires a skill marker, append this exact literal final line: Custom skills applied: oob-perf-analysis.