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Orchestrates modular PyTorch profiler trace analysis with TraceLens: generates perf reports, prepares category data, runs system-level and compute-kernel subagents in parallel, validates outputs, and writes a prioritized stakeholder report (analysis.md). Use when the user asks to follow the analysis orchestrator, run the agentic analysis workflow, analyze a trace, compare two traces, or mentions standalone or comparative TraceLens analysis.
npx skill4agent add amd/skills tracelens-analysis-orchestratoranalysis.md<prefix>{CMD}tee0. Query User Inputs (Platform, Trace Path(s), Analysis Mode, Environment Setup)
1. Generate Performance Report (branches on analysis mode: training vs inference then, comparison scope)
2-5. Prepare Category Data (GPU Util, Top Ops, Tree Data, Multi-Kernel Data, Category Filtering)
6. System-Level Analysis (PARALLEL) → system_findings/
7. Compute Kernel Subagents (PARALLEL) → category_findings/
7.5. Aggregate → priority_data.json::findings[]
8. Validate Subagent Outputs
9. load_findings + Model Identification (subagent) → metadata/model_info.json
10. Render performance PNG if agent_extension.py is absent
11. Generate analysis.md (orchestrator writes via <prefix> tee), optional extension, embed PNG<output_dir>/cache/cmd_prefix.txt{CMD}TraceLens/Agent/Analysis/skills/analysis-orchestrator/agents/<name>.md<output_dir>/analysis.mdsystem_findings/category_findings/category_data/metadata/perf_report*.xlsxTraceLens/Agent/Analysis/skills/analysis-orchestrator/agents/*.md