AMD Skills

Browse the Skill Catalog ->

AMD Skills provide agents with knowledge, scripts, and conventions for working with AMD hardware and software.

Skills in this repository follow the standardized Agent Skills format and are designed to interoperate with the major coding agents like Cursor, Claude Code, OpenAI Codex, and Gemini CLI.

Important

This catalog is being built in the open and will evolve frequently as skills, categories, and descriptions take shape. Some skills may be in Tech Preview; see the underlying product for status.

Installation

Install AMD Skills with the skills CLI via npx. No clone or manual copying required.

npx skills add amd/skills

This prompts you to pick a skill and an install destination. To install a specific skill into specific agents, pass --skill with one or more --agent flags (e.g. cursor, claude-code, codex):

npx skills add amd/skills --skill local-ai-use --agent claude-code

Browse everything available before installing:

npx skills add amd/skills --list

npx requires Node.js. Prefer to do it by hand? See Manual installation.

Using a skill

Once a skill is installed, reference it in plain language while talking to your agent. For example:

• "Use AMD Skills to learn how to generate images locally instead of burning cloud tokens."
• "Use AMD Skills to deploy this LLM for inference on my AMD Instinct GPUs."

In most cases the agent picks the right skill on its own from the description; explicit invocation is a fallback, not a requirement.

For hands-on, step-by-step guides that show a skill in action, see the walkthroughs.

The catalog

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
hyperloom-workload-optimizer	Autonomously optimizes LLM inference on AMD GPUs.	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
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

What is a skill?

A skill is a self-contained folder that bundles everything an agent needs to perform a focused task: instructions, helper scripts, prompts, templates, and references. At its core is a SKILL.md file with YAML frontmatter, a name, and a short description that tells the agent when the skill should activate, followed by the guidance the agent reads while the skill is in use.

skills/
  <skill-name>/
    SKILL.md
    skill-card.md
    scripts/       # optional
    references/    # optional

When an agent decides a skill is relevant (or you invoke it explicitly), it loads that SKILL.md and follows the instructions inside. Descriptions stay in context cheaply; the full body of a skill only loads when the task actually matches.

Every skill also ships a skill-card.md: a short, human-facing governance card (Description, Owner, License) that tells a reviewer what the skill is and who stands behind it without reading the source. See docs/skill-requirements.md.

Why a skill, not a doc?

Documentation describes an API surface: every flag, every option, neutral by design. A skill encodes the opinionated path: which flags, which container image, which gfx target, which environment variables, in what order. It captures the decisions a senior AMD engineer makes without thinking, in a form the agent can apply consistently across teams and repositories.

Skills earn their keep on repeated, opinionated workflows, exactly where the AMD stack lives.

Catalog federation

The AMD stack is large and moves fast. ROCm, HIP, Ryzen AI, and framework integrations each have their own team, release cadence, and validation matrix. So skills here are federated: each skill is owned and versioned by the team that owns the product it describes, and this repository is the catalog that brings them together.

                ┌─────────────────────────────────────────────────────┐
                │                amd/skills (this repo)               │
                │                                                     │
                │   skills/         .github/         .*-plugin/       │
                │   vendored copies federation.json  agent manifests  │
                └──────────────────────┬──────────────────────────────┘
                                       │  one install
                                       ▼
                              your AI coding agent
                                       ▲
                                       │  resolves pointers to
       ┌───────────────┬───────────────┼───────────────┬────────────────┐
       │               │               │               │                │
   ROCm/ROCm       ROCm/HIP        Ryzen AI repo   lemonade-sdk    ...more
  rocm-doctor/    cuda-to-hip/    ryzen-ai-tools/   local-ai-app-   product
   gfx-target-...  triton-amd-...  ...               integration/    repos

.github/federation.json is the whole registry: each entry names a source repo and the exact path of every skill folder to vendor from it. Sources are tracked at main only, so nothing reaches users that the owning team has not already merged.

The federate-skills workflow runs nightly and on demand. It clones each declared repo, compares a content hash of the upstream skill folder against the hash recorded in the vendored copy's .federated.json, and re-vendors only the skills that actually changed. When something did change it regenerates the agent manifests and opens a pull request titled Bump <skill> to <short commit>, where the usual validate checks apply as they would to any other pull request. A quiet night produces no diff and therefore no pull request, so every bump that lands is a reviewed commit.

A vendored skill mirrors its upstream folder with one exception: for now federation does not carry the skill's evals/ folder in either direction, so the datasets this repo grades skills against live and are maintained here.

See CONTRIBUTING.md to register a repo.

Manual installation

Until marketplace integration lands, install skills manually: clone this repo, then copy (or symlink) the skill folders you want from skills/ into your agent's skills directory. Each agent discovers SKILL.md automatically.

git clone https://github.com/amd/skills.git amd-skills
cp -r amd-skills/skills/local-ai-use <agent-skills-dir>/

Agent	Skills directory (personal / project)
Cursor	~/.cursor/skills/ or .cursor/skills/
Claude Code	~/.claude/skills/ or .claude/skills/
Codex	$HOME/.agents/skills or $REPO_ROOT/.agents/skills

Contributing

Contributions are welcome from AMD engineers and selected partners.

See CONTRIBUTING.md for the step-by-step instructions, then docs/skill-requirements.md for the rules CI enforces, docs/best-practices.md for writing guidance, and docs/evals.md for how skills are graded.

License

Released under the MIT License. See LICENSE for details.

Copyright (C) 2026 Advanced Micro Devices, Inc. All rights reserved.