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。

React and Next.js performance guidance for writing, reviewing, and refactoring production UI code.

30K
Stars
88/100
信頼
カテゴリ: coding-agents監査

An AI agent skill that provides design intelligence and UI/UX guidelines for building professional interfaces across multiple platforms.

107K
Stars
87/100
信頼
カテゴリ: development監査

The Modular Platform (includes MAX & Mojo)

26K
Stars
77/100
信頼
カテゴリ: ml-automation監査

Worktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows

5.5K
Stars
74/100
信頼
カテゴリ: coding-agents監査

Receive notifications when an image is updated on a Docker registry

4.7K
Stars
76/100
信頼
カテゴリ: devops監査

A long-form article / blog post — masthead, hero image placeholder, article body with figures and pull quotes, author byline, related posts. Use when the brief asks for "blog", "article", "post", "essay", or "case study".

90K
Stars
77/100
信頼
カテゴリ: design-creative監査

Audio generation skill — jingles, beds, voiceover, and sound effects. Routes music requests to Suno V5 / Udio / Lyria, speech to MiniMax TTS / FishAudio / ElevenLabs V3, and SFX to ElevenLabs SFX or AudioCraft. Output is one MP3/WAV file saved to the project folder.

90K
Stars
69/100
信頼
カテゴリ: design-creative監査

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

51K
Stars
68/100
信頼
カテゴリ: design-creative監査

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

34K
Stars
77/100
信頼
カテゴリ: data-analysis監査

Claude skill: Prototype → Figma. Analyzes a Claude Code prototype, maps components to your Figma design system via search + Code Connect, explodes each interaction flow into state-by-state frames, and annotates triggers, transitions, and edge cases, making prototypes reviewable by PMs, designers, and engineers without running code.

138
Stars
76/100
信頼
カテゴリ: utility監査

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

34K
Stars
78/100
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