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.

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.

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.

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.

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.

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.

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.

Serves AI models on AMD Instinct GPU hardware using vLLM. Use this skill whenever the user wants to run, serve, deploy, start, host, or launch a language model on an AMD GPU, AMD Instinct, MI300X, MI325X, MI350X, or MI355X. Also use when the user mentions vLLM on ROCm, vLLM on AMD, serving on HBM, or asks how to get a model running on AMD data center hardware. Use when the user asks "run Qwen3", "serve DeepSeek", "start a vLLM endpoint", "get a model running on my AMD machine", or any similar phrasing. Handles the full flow: GPU detection, environment validation, vLLM configuration, launch, and health verification. Do not use for NVIDIA GPUs, consumer AMD GPUs (RX series, Radeon), Ryzen AI, NPU, MI250X, or MI100.