Skills catalog

This page lists the skills currently available in the AMD Skills repository, organized by focus area.

The initial catalog is organized into three focus areas, spanning the full stack from client to cloud. This catalog is expected to grow significantly as more skills land.

Client-native

Run and optimize on Ryzen AI.

Skill	What it does	Source
local-ai-use	Route image generation, text-to-speech, and speech-to-text through a local AI server to reduce token cost.	in-repo
local-ai-app-integration	Integrate local AI into cloud LLM apps for offline support, better privacy, and lower API costs.	in-repo

Cross-stack

Cross-stack skills, from client to cloud.

Skill	What it does	Source
rocm-doctor	Diagnose ROCm, HIP, PyTorch, or llama.cpp failures on AMD GPUs (Linux and Windows) against a closed list of known misconfigurations, then fix with consent or route upstream. Thin driver over the rocm CLI (examine, diagnose, or fix).	planned
lemonade-router-builder	Set up a Lemonade model router that handles requests based on content, sensitivity, or required capabilities.	in-repo
hrr-replay-analysis	Record, replay, and analyze GPU workload behavior on ROCm across AMD Instinct, Radeon, and Ryzen hardware using HIP Record and Replay archives.	planned

Server-native

Run and optimize on AMD Instinct.

Skill	What it does	Source
serving-llms-on-instinct	Deploy LLM inference on AMD Instinct GPUs end-to-end: detect hardware (or onboard via AMD Developer Cloud), validate model fit, apply the right vLLM recipe, and launch a benchmarked endpoint. SGLang and engine or backend selection in later phases.	in-repo
serving-llms-on-epyc	Serve LLMs on AMD EPYC CPUs with vLLM and zentorch, in a container (Docker or Podman) or conda. Handles CPU detection, runtime and env validation, vLLM model-support and RAM-fit checks, hardware-sized threads and KV, launch, and health verification. Single instance; reports and stops on failure.	in-repo
hyperloom-workload-optimizer	Set up Hyperloom and autonomously optimize end-to-end LLM inference throughput on AMD Instinct GPUs, reporting a validated gain.	in-repo
magpie-kernel-evaluator	Evaluate GPU kernel correctness and performance, compare kernel implementations, and benchmark vLLM or SGLang inference with profiling, TraceLens, and torch-trace gap analysis.	Magpie
tracelens-analysis-orchestrator	Orchestrate modular PyTorch profiler trace analysis with TraceLens: generate perf reports, run system-level and compute-kernel subagents in parallel, and write a prioritized stakeholder report.	TraceLens