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: play-console

英語版ディレクトリ

KubeSphere is a comprehensive container platform designed for managing Kubernetes across multi-cloud, datacenter, and edge environments.

17K
Stars
77/100
信頼
カテゴリ: utility監査

Turn one topic into a finished Vox-style paper-collage explainer/ad video — automated end to end on Atlas Cloud + ffmpeg. An agent skill.

1.3K
Stars
86/100
信頼
カテゴリ: utility監査

给纯文本 LLM agent 装上眼睛:图片问答、OCR、截图分析、视觉定位等一套视觉工具箱 + skill,并可无缝接入 Codex、Claude Code、OpenCode、Pi | Give text-only LLM agents vision: image Q&A, OCR, screenshot understanding, visual grounding, image-to-SVG - a vision toolkit & skill, with drop-in integration for Codex, Claude Code, OpenCode, Pi

321
Stars
77/100
信頼
カテゴリ: utility監査

A curated library of 68 plug-and-play product management skills for AI agents, covering the complete product lifecycle with templates, workflows, and sample outputs.

509
Stars
84/100
信頼
カテゴリ: productivity監査

Extract one time password (OTP) secrets from QR codes exported by two-factor authentication (2FA) apps such as "Google Authenticator". The exported QR codes from authentication apps can be captured by camera, read from images, or read from text files. The secrets can be exported to JSON or CSV, or printed as QR codes to console.

1.6K
Stars
77/100
信頼
カテゴリ: data-analysis監査

Console for mobile browsers

21K
Stars
74/100
信頼
カテゴリ: coding-agents監査

A reusable AI agent skill for Claude Code that acts as a Dungeon Master for D&D 5e campaigns with persistent state and optional cinematic display.

124
Stars
77/100
信頼
カテゴリ: utility監査

A library of reusable AI agent skills following the Agent Skills standard, installable across multiple AI development tools.

118
Stars
74/100
信頼
カテゴリ: utility監査

Simple and flexible progress bar for Jupyter Notebook and console

1.1K
Stars
76/100
信頼
カテゴリ: coding-agents監査

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査