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: init-lua

英語版ディレクトリ

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

Staggeringly powerful macOS desktop automation with Lua

16K
Stars
80/100
信頼
カテゴリ: automation監査

An open source personal productivity platform built on Markdown, turbo charged with the scripting power of Lua

5.5K
Stars
86/100
信頼
カテゴリ: document-processing監査

A Vault swiss-army knife: A CLI tool to init, unseal and configure Vault (auth methods, secret engines).

2.3K
Stars
73/100
信頼
カテゴリ: devops監査

A geospatial analytics skill for AI agents like Claude, Codex, and Copilot, enabling map-based queries on PostGIS, BigQuery, Snowflake.

571
Stars
84/100
信頼
カテゴリ: data監査

AI equity research agent with resilient workflows, Redis Lua single-flight, pgvector RAG, versioned reports, evidence tracing, and RAG evaluation.

1.2K
Stars
79/100
信頼
カテゴリ: rag-knowledge監査

The missing CMake project initializer

2.5K
Stars
76/100
信頼
カテゴリ: development監査

A penetration testing skill for AI agents featuring staged workflows, case memory, and activation phrase for local sandbox and CTF environments.

168
Stars
80/100
信頼
カテゴリ: security監査

搜索、安装和协调创建 Claude Code Agent Skills。当用户想要搜索技能、安装工具、创建自定义 Skill,或者说"find a skill"、"搜索技能"、"帮我做个 skill"、"create a skill"时触发。也适用于用户说"有没有做 X 的工具"、"我想扩展 Agent 能力",或当前能力不足需要先查找可复用方案的场景。

51K
Stars
70/100
信頼
カテゴリ: research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

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