17 boosters for "hardware" — open source, verified from GitHub, ready to install
These instructions are derived from and apply to all AI-assisted contributions to the Zephyr RTOS repository. the file's native comment syntax:
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
This skill is for running evaluations against models on the Hugging Face Hub on local hardware. It does not cover: If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the skill and pass it one of the local scripts in this skill.
Build and publish a Gradio demo on Hugging Face Spaces that runs inference with a user-provided LoRA. Use whenever someone asks to create, generate, ship, or publish "a Space", "a demo", "a Gradio app", or "a playground" for a LoRA — whether the base model is Qwen-Image, Qwen-Image-Edit, LTX, or ano
Hugging Face Spaces host machine-learning applications. There are 1M+ today; each Space is a git repo. This skill covers creating, building, debugging, and maintaining them. Before anything else: 1. Check the CLI is installed: . If not, .
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Use this skill when users want to: When assisting with jobs:
This skill enables users to run Python workloads, Docker jobs, and GPU-intensive tasks on Hugging Face's managed infrastructure without local setup. It's valuable for ML engineers, data scientists, and developers needing cloud compute for training, inference, and batch processing.
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
Implement UVM-based hardware verification testbenches — stimulus generation, scoreboarding, coverage-driven verification, and assertion-based testing.
"name": "nextboard-hardware-solution", "description": "Hardware solution design skills and workflows for embedded and hardware engineers.", "component-selection"
"description": "Agent skill for the KSafe Kotlin Multiplatform encrypted persistence library — setup, usage patterns, anti-patterns, and debugging.", "name": "Ioannis Anifantakis"
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
A progressive-disclosure skill for ROS 2 development — from first workspace to production fleet deployment. Each section below gives you the essential decision framework; detailed patterns, code templates, and anti-patterns live in the
Use TIA Portal Openness as the execution boundary: any AI proposes structured actions, generated .NET code performs them through , and every destructive or plant-impacting operation is explicit, logged, and reviewable. 1. Identify the project path and exact operation. Detect the installed TIA Portal
"name": "tigerpass", "description": "Hardware-secured crypto wallet and trading terminal for AI agents — trade Hyperliquid perps, Polymarket predictions, swap tokens, bridge USDC, and conduct E2E encrypted agent-to-agent commerce. Keys in Apple Secure Enclave.", "name": "TigerPass",
A Windsurf-specific development ruleset emphasizing accessibility-first practices, clear documentation, and organized code structure for building fitness applications with integrated health tracking features.
