تشغيل أي مهارة في Manus بنقرة واحدة

tracelens-analysis-orchestrator

النجوم١٢

التفرعات٦

آخر تحديث١٦ يوليو ٢٠٢٦ في ٢٢:١٨

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.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

amd

amd/skills

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

مستكشف الملفات

20 ملفات

SKILL.md

readonly

name

tracelens-analysis-orchestrator

description

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 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)
2-5. 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

Rules

Subagents: Use the Task tool only where reference.md says “subagent” (Steps 6, 7, 9). The orchestrator runs everything else, including Step 7.5, 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.

المزيد من هذا المستودع

نفس المستودع

local-ai-app-integration

amd/skills

Integrates local AI capabilities into applications using Embeddable Lemonade. Use when the user wants to add local AI, offline AI, private AI, on-device AI, a local LLM, local chat, embeddings, image generation, speech-to-text, or text-to-speech to an app; replace or supplement OpenAI, Anthropic, Ollama, or other cloud AI APIs with a local backend; bundle AI inference into an app installer; or mentions Lemonade, `lemond`, embeddable lemonade, Ryzen AI, NPU/iGPU/dGPU inference, or auto-optimizing local AI.

2026-07-2012

local-ai-use

amd/skills

Routes image generation, text-to-speech, and speech-to-text through a local Lemonade Server so the agent stops paying for cloud calls on those modalities. Use when the user wants to save tokens, save cost, or save money using local AI; default to local, offline, on-device, or private image generation, transcription, or text-to-speech in this workspace; stop using DALL-E, Whisper-as-a-service, ElevenLabs, or other paid multimodal APIs; route the agent's image, TTS, or STT tool calls to a local model; or mentions Lemonade Server, OmniRouter, SD-Turbo, kokoro, Whisper, Ryzen AI, NPU/iGPU/dGPU inference, or "use local for images but cloud for chat". Run once per workspace; the rule it installs handles every later request.

2026-07-1712

serving-llms-on-epyc

amd/skills

Serves a language model on an AMD EPYC CPU host using vLLM with the zentorch backend, in a container (Docker or Podman) or a conda env. Use whenever the user wants to run, serve, deploy, start, host, or launch an LLM on AMD EPYC, Zen CPU, "vLLM on CPU", "zentorch serving", or "serve a model without a GPU". Use for "serve Qwen on EPYC", "start a CPU vLLM endpoint", "run an OpenAI server on my EPYC box", or similar. Handles the full single-instance flow: detect the CPU (incl. EPYC generation), validate the runtime/env, check vLLM supports the model (via vLLM's registry, not a modality blocklist), check it fits host RAM, size CPU threads/KV/NUMA from the hardware, confirm the plan with the user, launch, and poll until the endpoint is responsive. Single instance, single socket (pinned to one socket + its memory; vLLM scales poorly across sockets). Does NOT debug failures and does NOT retry -- it reports and stops. Do not use for GPU/Instinct (use serving-llms-on-instinct) or multi-node.

2026-07-1312

magpie-kernel-evaluator

amd/skills

Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.

2026-06-2312

apu-memory-tuner

amd/skills

Inspects and tunes the shared-vs-dedicated memory split on AMD Ryzen APUs with unified memory (UMA) so larger LLMs and image-gen models fit on the iGPU, or so reserved GPU memory is returned to the CPU. Use when the user mentions Ryzen AI, Strix Halo / Strix Point / Krackan / Phoenix / Hawk Point, Ryzen AI Max, gfx1150 / gfx1151 / gfx1152, integrated Radeon, iGPU memory, UMA Frame Buffer Size, AMD Variable Graphics Memory, VGM, GTT, GART, TTM, pages_limit, amd-ttm, amd-debug-tools, "shared GPU memory", "dedicated GPU memory", carve-out, "not enough VRAM", "out of VRAM", "GPU OOM", llama.cpp on iGPU, ROCm on APU; or asks how much memory the iGPU can use, how to give the iGPU more memory, how to balance memory between CPU and GPU on UMA, or how to change the BIOS UMA reservation. Read-only diagnostics work everywhere; tuning runs automatically on Linux via `amd-ttm` and prints guided BIOS steps on Windows. Do not use for discrete Radeon cards, Intel iGPUs, or Apple Silicon -- it is APU-only.

2026-06-2312

rocm-doctor

amd/skills

Diagnoses why ROCm, the HIP SDK, PyTorch, or llama.cpp is broken on an AMD GPU on Linux or Windows, and either applies a low-risk fix with consent or hands back the exact next step. Also routes Lemonade, LM Studio, and Ollama issues to the right upstream channel. Use when the user reports that ROCm or HIP isn't working, torch.cuda.is_available() is False Ryzen AI, rocminfo or hipInfo can't see the GPU, or hits hipErrorNoBinaryForGpu, HSA_STATUS_ERROR_INVALID_ISA, invalid device function, missing amdhip64_6.dll, vcruntime140_1.dll, or libamdhip64.so, cannot open /dev/kfd, ROCk module not loaded, an Adrenalin driver too old for the HIP SDK, or a ROCm wheel that doesn't recognize gfx1151, gfx1150, or gfx1103; or mentions HSA_OVERRIDE_GFX_VERSION, HIP_VISIBLE_DEVICES, PYTORCH_ROCM_ARCH, render-group permissions, amdgpu blacklist, Secure Boot, iGPU/dGPU collisions, or multi-GPU hangs. Do not use for non-AMD GPUs, performance tuning, or ROCm-on-WSL2.

2026-06-2312

name

tracelens-analysis-orchestrator

description

Analysis orchestrator

Full procedure

Follow 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)
2-5. 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

Rules

Subagents: Use the Task tool only where reference.md says “subagent” (Steps 6, 7, 9). The orchestrator runs everything else, including Step 7.5, 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.