amd-skills

Turns a natural-language description of routing intent into a valid Lemonade `collection.router` policy JSON. The skill generates and validates the JSON only - it does not register it or call the live server. Use when the user wants to route requests between models ("route sensitive queries to X and everything else to Y"), generate a router/hybrid-router config or policy, author a collection.router JSON, split traffic between a small local model and a big/cloud model, add PII/jailbreak/topic classifiers to routing, or mentions Lemonade Router, routing rules, routing.router, candidates/default_model, keywords_any, semantic_similarity, or LLM-as-router. Fills every field the user did not specify with safe defaults.

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.

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.

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.

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.

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.