Skill-Verzeichnis

Wiederverwendbare Skills für AI Agents entdecken.

Durchsuche reale GitHub-Skills nach Aufgabe und prüfe Stars, Trust, Audit, Kategorie und Installationspfad vor der Verwendung.

Jede Empfehlung bleibt mit ihrem Repository, Audit und Installationspfad nachvollziehbar.

Suchergebnisse: hardware

Englisches Verzeichnis

AI IDE for hardware development, support Arduino, MicroPython, ESP32, STM32, RP2040, Nrf5x...

3.8K
Stars
86/100
Trust
Kategorie: robotics-iotAudit

A collection of agent skills for CAD, robotics and hardware design

8.3K
Stars
81/100
Trust
Kategorie: agent-skillsAudit

Community maintained hardware plugin for vLLM on Ascend

2.2K
Stars
80/100
Trust
Kategorie: developmentAudit

Next-gen AI+IoT framework for T2/T3/T5AI/ESP32/and more – Fast IoT and AI Agent hardware integration

1.6K
Stars
76/100
Trust
Kategorie: support-automationAudit

A GitHub Action for installing, configuring and running hardware-accelerated Android Emulators on macOS virtual machines.

1.3K
Stars
85/100
Trust
Kategorie: github-automationAudit

Open-source private logbook with a local agentic layer. Long-living AI agents read what you record and propose what to do next. Hardware permitting, the models runs locally too. Matrix + Vodozemac for end-to-end encrypted sync between your own devices.

1.1K
Stars
84/100
Trust
Kategorie: legal-complianceAudit

Official AMD catalog of reusable AI agent skills optimized for AMD hardware and compatible with Cursor, Claude Code, and OpenAI Codex.

230
Stars
77/100
Trust
Kategorie: coding-agentsAudit

A hardware-aware Codex/WorkBuddy skill that automates local MiniMax H3 video generation through ComfyUI, handling model selection, installation, and low-VRAM configuration.

126
Stars
80/100
Trust
Kategorie: design-creativeAudit

Generate a controlled local narration workflow with auditions, version tracking, and subtitle-ready final audio.

1.3K
Stars
64/100
Trust
Kategorie: Video CreationAudit

EVA OS — A real-time multimodal AIOS for next-generation hardware, enabling your devices being “alive” and as intelligent as a real brain.

682
Stars
72/100
Trust
Kategorie: robotics-iotAudit

Master AI inference, AI agent harness systems, and hardware engineering — then design a physical AI chip. That is the goal.

248
Stars
72/100
Trust
Kategorie: robotics-iotAudit

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

2.5K
Stars
74/100
Trust
Kategorie: data-analysisAudit