How do I install the Oob Perf Analysis skill?

Run npx skillmds@latest add intel/oob-perf-analysis in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Oob Perf Analysis safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Oob Perf Analysis?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Oob Perf Analysis free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Oob Perf Analysis?

intel (@intel) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Oob Perf Analysis by intel — Agent Skill

Name: Oob Perf Analysis
Author: intel

Oob Perf Analysis

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.

intel fb8d060 9 files · 38.1 KB Updated Sep 17, 2026

File contents

OOB Perf Analysis Skill

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):

Per-model markdown reports under agent_space_xpu/reports/<session>/models/
Fleet summary under agent_space_xpu/reports/<session>/summary_eager_inference.md
Graph consistency report under agent_space_xpu/reports/<session>/graph_consistency_eager_inference.md
Insights summary under agent_space_xpu/reports/<session>/insights_summary.md

References

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

Usage Modes

Per-Model Report Mode

User has raw artifacts and wants model-level T1/T2/R analysis.

Follow: methodology.md → inputs.md → per-model-report.md

Fleet Summary Mode

User wants fleet-wide comparison, model scorecards, op ranking, or projection-quality aggregation.

Follow: fleet-summary.md (references graph-consistency.md for Section 7)

Graph Consistency Mode

User wants to compare CUDA vs XPU computational graphs.

Follow: graph-consistency.md

Insights Mode

Per-model reports and fleet summary exist; user wants a concise developer-facing summary.

Follow: insights.md

Troubleshooting Mode

User asks why R is abnormal, why traces disagree, or why results look suspicious.

Follow: troubleshooting.md

Operating Rules

Read methodology.md before computing any metric.
Read inputs.md before accessing any artifact.
Report structure is deterministic — same inputs produce same section layout.

If a calling workflow explicitly requires a skill marker, append this exact literal final line: Custom skills applied: oob-perf-analysis.

---name: oob-perf-analysisdescription: 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.---# OOB Perf Analysis SkillAnalyzes 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):- Per-model markdown reports under `agent_space_xpu/reports/<session>/models/`- Fleet summary under `agent_space_xpu/reports/<session>/summary_eager_inference.md`- Graph consistency report under `agent_space_xpu/reports/<session>/graph_consistency_eager_inference.md`- Insights summary under `agent_space_xpu/reports/<session>/insights_summary.md`## References| File | Purpose ||------|---------|| [methodology.md](references/methodology.md) | T1/T2/R definitions, formulas, classification thresholds || [inputs.md](references/inputs.md) | Input file formats, completeness rules, output paths || [per-model-report.md](references/per-model-report.md) | Per-model analysis steps and 5-section report structure || [fleet-summary.md](references/fleet-summary.md) | Fleet aggregation steps and 7-section report structure || [graph-consistency.md](references/graph-consistency.md) | Graph consistency analysis || [insights.md](references/insights.md) | Developer-facing insights summary || [troubleshooting.md](references/troubleshooting.md) | Diagnosing abnormal R, trace issues, and data problems |## Usage Modes### Per-Model Report ModeUser has raw artifacts and wants model-level T1/T2/R analysis.Follow: `methodology.md` → `inputs.md` → `per-model-report.md`### Fleet Summary ModeUser wants fleet-wide comparison, model scorecards, op ranking, or projection-quality aggregation.Follow: `fleet-summary.md` (references `graph-consistency.md` for Section 7)### Graph Consistency ModeUser wants to compare CUDA vs XPU computational graphs.Follow: `graph-consistency.md`### Insights ModePer-model reports and fleet summary exist; user wants a concise developer-facing summary.Follow: `insights.md`### Troubleshooting ModeUser asks why R is abnormal, why traces disagree, or why results look suspicious.Follow: `troubleshooting.md`## Operating Rules1. Read `methodology.md` before computing any metric.2. Read `inputs.md` before accessing any artifact.3. Report structure is deterministic — same inputs produce same section layout.If a calling workflow explicitly requires a skill marker, append this exact literal final line:Custom skills applied: oob-perf-analysis.

Frequently asked questions