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intelintel/torch-xpu-ops/.claude/skills/oob-perf-analysis/SKILL.md

oob-perf-analysis

Skill121 starsChanged 5 months agoApache-2.0View on GitHub

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.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 1 is distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

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

    line 34
  2. Very specific:

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

    line 40
  3. Very specific:

    Read methodology.md before computing any metric.

    line 62
  4. Very specific:

    Read inputs.md before accessing any artifact.

    line 63
  5. Distinctive:

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

    line 66

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

FilePurpose
methodology.mdT1/T2/R definitions, formulas, classification thresholds
inputs.mdInput file formats, completeness rules, output paths
per-model-report.mdPer-model analysis steps and 5-section report structure
fleet-summary.mdFleet aggregation steps and 7-section report structure
graph-consistency.mdGraph consistency analysis
insights.mdDeveloper-facing insights summary
troubleshooting.mdDiagnosing 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

  1. 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.

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