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Source Adb4ab6cf328eeb7d859569d
ASecurityOrchestrates 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.
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- Added November 7, 2026
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[](https://www.skillsdirectory.com/skills/gutsofpratik-source-adb4ab6cf328eeb7d859569d)---
name: tracelens-analysis-orchestrator
description: >-
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.
license: MIT
---
<!--
Copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved.
See LICENSE for license information.
-->
# Analysis orchestrator
Coordinate **system-level** analysis (CPU/idle, kernel fusion, multi-kernel / comm / memcpy) and **compute-kernel** analysis (GEMM, SDPA, elementwise, etc.): one trace load, shared prep, parallel subagents, then aggregation into `analysis.md`.
## Full procedure
Follow **[reference.md](reference.md)** for every step (user prompts, `<prefix>` / `{CMD}` usage, CLI commands, subagent launch text, validation, report `tee` order, plot embedding, and trace diagnostics).
## Workflow index
```
0. 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. Trace-Quality Gate → Deterministic fallback analysis
3-6. Prepare Category Data (GPU Util, Top Ops, Tree Data, Multi-Kernel Data, Category Filtering)
7. System-Level Analysis (PARALLEL) → system_findings/
8. Compute Kernel Subagents (PARALLEL) → category_findings/
8.5. Aggregate → priority_data.json::findings[]
9. Validate Subagent Outputs
10. load_findings + Model Identification (subagent) → metadata/model_info.json
11. Render performance PNG if agent_extension.py is absent
12. Generate analysis.md (orchestrator writes via <prefix> tee), optional extension, embed PNG
```
## Rules
- **Subagents:** Use the Task tool **only** where reference.md says “subagent” (Steps **7**, **8**, **10**). The orchestrator runs everything else, including Step 8.5, using the command prefix from `<output_dir>/cache/cmd_prefix.txt` (`{CMD}` substitution).
- **Language:** Prefer vendor-agnostic terms (GPU kernels, collective communication, vendor GEMM library, DNN primitives, GPU graph). When quoting trace data, real kernel names are fine.
- **Subagent prompts:** Point each subagent at the checked-in agent file under `TraceLens/Agent/Analysis/skills/analysis-orchestrator/agents/<name>.md` (see reference.md for exact paths and prompt shells).
## Primary outputs
- **Deliverable:** `<output_dir>/analysis.md`
- **Internals:** `system_findings/`, `category_findings/`, `category_data/`, `metadata/`, `perf_report*.xlsx`, CSV folders — see package README for layout.
## Agent layout
Project subagents ship with this skill: `TraceLens/Agent/Analysis/skills/analysis-orchestrator/agents/*.md`.
Files in this skill
- .federated.json
- PROVENANCE.json
- SKILL.md
- UPSTREAM-LICENSE.txt
- agents/convolution-analyzer.md
- agents/cpu-idle-analyzer.md
- agents/elementwise-analyzer.md
- agents/gemm-analyzer.md
- agents/generic-op-analyzer.md
- agents/kernel-fusion-analyzer.md
- agents/model-identification-agent.md
- agents/moe-analyzer.md
- agents/multi-kernel-analyzer.md
- agents/norm-analyzer.md
- agents/reduce-analyzer.md
- agents/sdpa-analyzer.md
- agents/triton-analyzer.md
- evals/evals.json
- evals/machine.yml
- reference.md
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