skills: Give AI Agents AMD Workflow Guidance

skills: Give AI Agents AMD Workflow Guidance

Summary

AMD Skills is a catalog of task-focused instructions and tools that help coding agents work with AMD hardware and software. Install selected skills into compatible agents when you need guidance for workflows such as local AI, ROCm troubleshooting, or LLM serving.

At a glance

Language
Python
License
MIT
Stars
386
Forks
39
Added to OSRepos
August 16, 2026
Last analyzed
October 3, 2026
View on GitHub

Topics

Click on any tag to explore related repositories

Use at your own risk

OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.

Overview

AMD Skills is a catalog of agent-ready workflows for AMD software and hardware. Each skill packages focused instructions and, where needed, scripts or references so an AI coding agent can follow an opinionated workflow rather than rely on general documentation alone.

It is intended for developers using coding agents across AMD's client-to-cloud stack. Use it when you want an agent to assist with a supported AMD task, such as running local AI or deploying inference, and check the individual skill for its scope and product status. The catalog is evolving, and some entries are planned or in tech preview.

Key Features

  • Follows the Agent Skills format, with a SKILL.md describing when and how an agent should apply a skill.
  • Provides workflows spanning Ryzen AI, ROCm, AMD Instinct GPUs, and EPYC CPUs.
  • Includes skills for local AI integration, LLM serving, quantization, troubleshooting, and performance analysis.
  • Supports installation through the skills CLI, with options to choose skills and target agents.
  • Targets compatible coding agents including Cursor, Claude Code, OpenAI Codex, and Gemini CLI.
  • Federates selected skills from product repositories, with upstream changes synchronized through a repository workflow.

Use Cases

  • An application developer wants an agent to route image, speech, or text-generation tasks to a local AI server instead of a cloud API.
  • An infrastructure engineer needs guidance deploying an LLM on AMD Instinct GPUs or EPYC CPUs.
  • A PyTorch user wants an agent-assisted workflow for quantizing a model with AMD Quark.
  • A developer diagnosing a ROCm or HIP issue wants a skill for checking known configuration problems, where the relevant skill is available.
  • A performance engineer wants help analyzing profiler traces or comparing GPU kernel behavior using the catalog's supported tools.

Project Facts

  • Language: Python
  • License: MIT
  • Stars: 386
  • Forks: 39
  • Topics: none listed
  • Archived: no

Getting Started

Install the catalog with Node.js and the skills CLI:

npx skills add amd/skills

See the repository README for selecting specific skills, manual installation, and catalog details.

Alternatives

  • awesome-agent-skills: An index of skills from many providers and for many workflows, rather than AMD-focused guidance for hardware and software.
  • skills: Provides portable skills for .NET and C# workflows instead of guidance for AMD hardware, ROCm, and LLM serving.
  • skills: Focuses on general engineering practices such as TDD and debugging, rather than AMD-specific tools and workflows.

Considerations

  • This is a growing catalog, not a general-purpose agent or standalone AMD runtime. The utility depends on whether a skill covers your specific task and product version.
  • The README notes that some skills may be in tech preview; check the underlying product's status and each skill's documentation before relying on it.
  • Installation through npx requires Node.js. Skills that operate on AMD software or hardware may have additional requirements described in their own instructions.
  • The repository reports a Python language, but the supplied installation path uses the Node.js-based skills CLI.

Source repository

Open the original repository on GitHub.

21 counted GitHub visits

View on GitHub

Related repositories

Similar repositories that may be relevant next.

agent-observability: Self-Hosted Observability for AI Coding Agents

agent-observability: Self-Hosted Observability for AI Coding Agents

October 3, 2026

agent-observability offers a robust, self-hosted OpenTelemetry stack designed for AI coding agents like Claude Code and OpenAI Codex. It ensures all telemetry data, including model requests, tool executions, and session activity, remains local within your environment. This comprehensive solution provides ready-made Grafana dashboards for deep insights into agent performance and usage.

ObservabilityAI AgentsOpentelemetry
OrcaReplay: Time Travel for AI Agents, Debugging and Evaluation

OrcaReplay: Time Travel for AI Agents, Debugging and Evaluation

October 2, 2026

OrcaReplay introduces "time travel" capabilities for AI agents, allowing developers to record, replay, fork, and debug any agent run with any model. It addresses the challenges of AI agent debugging by providing byte-for-byte reproducibility, offline analysis, and the ability to compare different models from specific checkpoints. This tool, built by the OrcaRouter.ai team, enhances observability and control over complex agent behaviors.

Agent DebuggingAI AgentsLLM Agents
Prime Agent: A Self-Improving RLM Agent for Coding and Autonomous Tasks

Prime Agent: A Self-Improving RLM Agent for Coding and Autonomous Tasks

October 2, 2026

Prime Agent is an open-source, self-improving Recursive Language Model (RLM) agent designed for coding workflows and long-running autonomous tasks. It integrates a persistent Python control environment with a durable harness state, allowing useful context and reusable patterns to persist across sessions. Built in Rust, this project aims to enhance developer productivity through programmatic control and autonomous capabilities.

RustAI AgentsLLM
OpenMake LLM: Self-Hosted AI Workspace for Local and Open-Weight LLMs

OpenMake LLM: Self-Hosted AI Workspace for Local and Open-Weight LLMs

October 1, 2026

OpenMake LLM is an open-source, self-hosted AI workspace for local and open-weight LLMs. It coordinates specialized models, autonomous agents, and tools for deep research and artifact generation. This platform supports vLLM, LiteLLM, and BYOK providers, offering a robust environment for managing AI workloads.

AI AgentsAI WorkspaceSelf Hosted AI
OS
OSRepos

Analysis and discovery of open source repositories. Find interesting projects and follow their updates.

Monitor your website with YourWebsiteScore

OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of third-party repository code is at your own risk. Always review source code, dependencies, licenses, and security implications before running anything.

© 2025 OSRepos. Built with Nuxt 3 and lots of ❤️