AMD Had Zero Agent Skills. I Built the First 10.

A developer built the first open-source collection of 10 agent skills for AMD ROCm GPU workloads, filling a gap where NVIDIA had 428+ skills on skills.sh and AMD had zero. The skills cover GPU setup, Docker configuration, inference deployment, benchmarking, and safety compliance, following the agentskills.io specification for compatibility with multiple AI coding agents.

NVIDIA has 428+ agent skills on skills.sh. AMD had zero. This is the story of building the first open-source collection of agent skills for AMD ROCm GPU workloads — and why it matters for the entire AI ecosystem. If you've used AI coding agents like Claude Code, OpenCode, Cursor, or Codex, you've probably encountered agent skills — reusable instruction sets that teach agents how to perform specific tasks. Skills are the building blocks of agent workflows: "set up my GPU", "deploy vLLM", "run YOLO inference", "check PPE compliance". The agent skills ecosystem is powered by skills.sh https://skills.sh , a registry that indexes skill repositories from GitHub. When you run npx skills add owner/repo , the CLI clones the repo and installs skills into your agent's configuration directory. Here's the problem: | GPU Vendor | Agent Skills on skills.sh | |---|---| | NVIDIA | 428+ | | AMD | 0 | Zero. Not a single skill for AMD ROCm. If you're a developer using AMD MI300X, MI250, or even a Radeon RX 7900 with an AI coding agent, you had nothing. Every GPU setup, every Docker configuration, every inference deployment required manual documentation lookup and copy-paste. I built amd-rocm-skills — 10 production-ready agent skills covering the full AMD ROCm GPU workflow: | Skill | What it does | |---|---| rocm-setup | Install, verify, and configure ROCm on AMD GPUs with PyTorch. Auto-detects NVIDIA CUDA and CPU fallback. | rocm-docker | Docker with AMD GPU passthrough --device=/dev/kfd , NVIDIA runtime, and CPU profiles. docker-compose multi-profile. | vllm-rocm-deploy | Deploy vLLM for LLM/VLM inference on ROCm. InternVL2, Qwen2-VL, LLaVA. OpenAI-compatible API. | yolo-rocm-deploy | YOLOv8 on PyTorch ROCm. Inference, model export ONNX, TorchScript , benchmarking. | | Skill | What it does | |---|---| video-pipeline-rocm | Video inference pipeline with GStreamer + ROCm. RTSP capture, hardware decode AMD VCN / NVIDIA NVDEC , frame extraction, batch inference. | vlm-rocm-inference | VLM inference directly with PyTorch on ROCm. InternVL2, Qwen2-VL. Multimodal text + image . | rocm-benchmark | GPU benchmarking: matmul, memory bandwidth, inference latency, VRAM monitoring. ROCm + CUDA + CPU comparison. | | Skill | What it does | |---|---| ppe-detection-pipeline | PPE Personal Protective Equipment detection in video for industrial safety. YOLOv8 + tracking + alerts webhook, MQTT, log . Multi-camera, multi-GPU. | ds132-compliance | Chilean DS 132 mining safety compliance checker. Zone-based EPP requirements, audit logging, compliance reports. | rocm-troubleshoot | Diagnostics and troubleshooting for ROCm. Error codes, compatibility checks, quick fixes, optimization checklist. | Every skill follows the agentskills.io https://agentskills.io/specification specification — the open standard for agent skills: skills/<skill-name / ├── SKILL.md Required: YAML frontmatter + instructions ├── scripts/ Required: executable Python/Bash scripts └── references/ Optional: technical documentation The SKILL.md frontmatter uses only standard, portable fields: --- name: rocm-setup description: Install, verify, and configure AMD ROCm on Linux for AI/ML workloads with PyTorch. Use this skill when setting up AMD GPUs MI300X, MI250, RX 7900 for GPU-accelerated PyTorch, verifying ROCm installation, or diagnosing GPU detection issues. Keywords: rocm, amd, gpu, pytorch, hip, setup, mi300x, detect-gpu, rocm-smi, rocminfo, cuda, check-rocm license: Apache-2.0 compatibility: Compatible with Claude Code, OpenCode, Codex, Cursor, Cline, Roo Code, Windsurf, Gemini CLI, and Kiro CLI. Requires Linux with AMD ROCm or NVIDIA CUDA GPU CPU fallback supported . metadata: version: "1.1.0" author: yechua-silva --- No Claude Code-specific fields. No context: fork , no agent: Explore , no model: claude-sonnet- . Every skill works identically across 9+ agents. The key differentiator: every skill supports three backends with automatic detection . python Auto-detection pattern used across all skills import torch if torch.cuda.is available : if torch.version.hip: backend = "rocm" AMD ROCm device = "cuda:0" torch.cuda works on both elif torch.version.cuda: backend = "cuda" NVIDIA CUDA device = "cuda:0" else: backend = "cpu" device = "cpu" This is the critical insight: PyTorch's torch.cuda API works on both AMD ROCm and NVIDIA CUDA . There is no torch.rocm . ROCm uses the standard torch.cuda namespace transparently. Use torch.version.hip to distinguish AMD from NVIDIA.| Component | AMD ROCm | NVIDIA CUDA | CPU | |---|---|---|---| | PyTorch | torch.cuda + torch.version.hip | torch.cuda + torch.version.cuda | device='cpu' | | vLLM | vllm-openai-rocm | vllm-openai | --device cpu | | Docker | --device /dev/kfd --device /dev/dri | --gpus all | No flags | | Video decode | VAAPI / VCN | NVDEC | avdec software | The rocm-docker skill includes a docker-compose.yml with three profiles: AMD ROCm docker compose --profile rocm up -d NVIDIA CUDA docker compose --profile nvidia up -d CPU fallback docker compose --profile cpu up -d | Agent | Supported | How | |---|---|---| | Claude Code | ✅ | .claude/skills/ | | OpenCode | ✅ | .agents/skills/ | | Codex | ✅ | .codex/skills/ | | Cursor | ✅ | .cursor/skills/ | | Cline | ✅ | .cline/skills/ | | Roo Code | ✅ | .roo/skills/ | | Windsurf | ✅ | .windsurf/skills/ | | Gemini CLI | ✅ | .gemini/skills/ | | Kiro CLI | ✅ | .kiro/skills/ | List available skills npx skills add yechua-silva/amd-rocm-skills --list Install a single skill npx skills add yechua-silva/amd-rocm-skills --skill rocm-setup --agent opencode --yes Install all skills in multiple agents npx skills add yechua-silva/amd-rocm-skills -a claude-code -a opencode -a cursor --yes After installing the rocm-setup skill, just tell your agent: "Set up this AMD server for GPU workloads" The agent will: detect-gpu.py to identify your backend ROCm, CUDA, or CPU check-rocm.sh for a full health check HIP VISIBLE DEVICES , ROCm PATH After installing ppe-detection-pipeline : "Detect PPE in this RTSP stream and alert when workers are missing helmets" The agent will: The detect-gpu.py script from rocm-setup is the foundation of all 10 skills. It detects the GPU backend in three levels: python Level 1: PyTorch import torch if torch.cuda.is available : if hasattr torch.version, 'hip' and torch.version.hip: backend = "ROCM" device name = torch.cuda.get device name 0 e.g., "AMD Instinct MI300X" elif torch.version.cuda: backend = "CUDA" device name = torch.cuda.get device name 0 e.g., "NVIDIA A100-SXM-80GB" Level 2: System commands import subprocess if backend == "unknown": try: result = subprocess.run "rocm-smi", "--showproductname" , capture output=True, text=True if result.returncode == 0: backend = "ROCM" except FileNotFoundError: pass try: result = subprocess.run "nvidia-smi", "--query-gpu=name", "--format=csv,noheader" , capture output=True, text=True if result.returncode == 0: backend = "CUDA" except FileNotFoundError: pass Level 3: CPU fallback if backend == "unknown": backend = "CPU" This three-level detection ensures every skill works on any machine — whether you have an MI300X with 192GB HBM3, an RTX 4090, or just a laptop CPU. This isn't just a collection of scripts. It's built to industry standards: npx skills add skills.sh.json Before any skill is merged: name field matches directory name kebab-case description includes keywords for agent matching description includes trigger phrases "Use when..." compatibility is a string, not a YAML list context , agent , model , hooks chmod +x Related Skills section| Metric | Value | |---|---| | Skills | 10 | | Total lines of content | ~32,000 | | Scripts Python + Bash | 26 | | Reference documents | 23 | | Compatible agents | 9+ | | GPU backends | 3 ROCm + CUDA + CPU | | License | Apache 2.0 | | References to specific projects | 0 fully agnostic | The AI ecosystem has a GPU diversity problem. NVIDIA dominates not just hardware, but the entire software tooling stack — documentation, tutorials, community, and now agent skills. AMD's MI300X is a phenomenal chip 192GB HBM3, competitive with H100 for many workloads , but the developer experience gap is real. Agent skills are the newest frontier of this gap. When a developer asks Claude Code to "set up my AMD GPU for PyTorch", the agent should know how. Without a skill, it hallucinates or gives generic advice. With a skill, it follows a tested, verified workflow. 10 skills won't close a 428-skill gap. But it's a start — and it's open source. Want to add a skill? Here's how: skills/your-skill-name/SKILL.md with frontmatter scripts/ with executable Python/Bash references/ with technical docsSee CONTRIBUTING.md https://github.com/yechua-silva/amd-rocm-skills/blob/main/CONTRIBUTING.md for the full guide. rocm-tuning — ROCm performance tuning HIPBLAS, RCCL, MIOpen onnx-rocm — ONNX Runtime with ROCm execution provider fsdp-rocm — Fully Sharded Data Parallel on AMD GPU triton-rocm — Triton kernels on ROCm composable-kernel — AMD CK for custom kernels npx skills add yechua-silva/amd-rocm-skills --list Repo: github.com/yechua-silva/amd-rocm-skills https://github.com/yechua-silva/amd-rocm-skills License: Apache 2.0 Built during AMD Developer Hackathon Act II — Pista Unicornio. If you're using AMD GPUs with AI agents, I'd love to hear your feedback.