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: conversation

英語版ディレクトリ

Turn the current conversation and codebase context into a structured implementation spec, then publish it to the configured project issue tracker.

165K
Stars
81/100
信頼
カテゴリ: coding-agents監査

Break a plan, spec, or conversation into independently actionable tracer-bullet tickets with explicit blocking relationships.

177K
Stars
81/100
信頼
カテゴリ: coding-agents監査

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

18K
Stars
87/100
信頼
カテゴリ: design-creative監査

Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.

3.3K
Stars
85/100
信頼
カテゴリ: devops監査

Export and Share your ChatGPT conversation history

2.6K
Stars
77/100
信頼
カテゴリ: document-processing監査

Voice AI SDK is a reusable Android library that gives any app a full voice-driven AI conversation pipeline in minutes. Voice Assistant + Android Voide AI + SDK + MVVM + Kotlin

2.6K
Stars
76/100
信頼
カテゴリ: agent-frameworks監査

A plugin for OpenCode that provides dynamic skill loading, context injection, and other tools for using reusable AI agent skills.

262
Stars
77/100
信頼
カテゴリ: coding-agents監査

Claude Code plugin that generates individualized knowledge systems from conversation. You describe how you think and work, have a conversation and get a complete second brain as markdown files you own.

3.5K
Stars
77/100
信頼
カテゴリ: document-processing監査

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation

1.1K
Stars
76/100
信頼
カテゴリ: support-automation監査

Installable agent skills for creating and managing reversible themes for AI desktop apps like Codex and WorkBuddy.

194
Stars
76/100
信頼
カテゴリ: design-creative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