Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: proxmox-ve

英語版ディレクトリ

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
信頼
カテゴリ: data-analysis監査

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
信頼
カテゴリ: ml-automation監査

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

6.0K
Stars
84/100
信頼
カテゴリ: devops監査

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
信頼
カテゴリ: design-creative監査

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
信頼
カテゴリ: devops監査

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

5.2K
Stars
76/100
信頼
カテゴリ: development監査

: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
信頼
カテゴリ: devops監査

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

1.3K
Stars
83/100
信頼
カテゴリ: devops監査

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
信頼
カテゴリ: devops監査

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
信頼
カテゴリ: research監査