Skill 디렉토리

AI Agent를 위한 재사용 가능한 Skill을 찾으세요.

작업으로 실제 GitHub Skill을 검색하고 사용 전에 Stars, 신뢰, 감사, 카테고리, 설치 경로를 확인하세요.

모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.

검색 결과: proxmox-ve

영문 디렉토리

Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.

12K
Stars
82/100
신뢰
카테고리: data-analysis감사

YC (S26) | AI that knows what you've seen, said, or heard. Records everything you do, say, hear 24/7, local, private, secure

19K
Stars
79/100
신뢰
카테고리: ml-automation감사

Real-time monitoring for Proxmox, Docker, and Kubernetes with AI-powered insights, smart alerts, and a beautiful unified dashboard

6.0K
Stars
84/100
신뢰
카테고리: devops감사

An agent skill that scans web projects for common AI-generated design patterns and suggests or applies fixes to remove them.

786
Stars
84/100
신뢰
카테고리: design-creative감사

Start your containers on demand, shut them down automatically when there's no activity. Docker, Docker Swarm Mode, Podman, Kubernetes and Proxmox LXC compatible.

2.8K
Stars
83/100
신뢰
카테고리: devops감사

A Claude Code skill that generates interactive HTML courses from any codebase for non-technical users.

5.2K
Stars
76/100
신뢰
카테고리: development감사

:house: Buildroot-based, cloud-free smart-home platform for a Homematic IP CCU (CCU3/ELV-Charly). Runs on Raspberry Pi & x86/ARM or as a virtual appliance (Proxmox VE, Home Assistant, Docker/LXC/K8s)...

1.8K
Stars
83/100
신뢰
카테고리: devops감사

High Performance Software-Defined Block Storage for container, cloud and virtualisation. Fully integrated with Docker, Kubernetes, Openstack, Proxmox etc.

1.3K
Stars
83/100
신뢰
카테고리: devops감사

Complete guide explaining how to build and run a virtualized small Kubernetes cluster with one Proxmox VE standalone node on a single computer.

1.2K
Stars
71/100
신뢰
카테고리: devops감사

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

34K
Stars
70/100
신뢰
카테고리: research감사