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: heart-rate

英語版ディレクトリ

Integration platform for AI agents with 250+ app connectors

29K
Stars
80/100
信頼
カテゴリ: integrations監査

:black_heart: Create and share beautiful images of your source code

36K
Stars
77/100
信頼
カテゴリ: design-creative監査

Like htop, but for AI coding agents. Monitor Claude Code & Codex CLI sessions, tokens, context window, rate limits, and ports in real-time.

2.8K
Stars
79/100
信頼
カテゴリ: agent-frameworks監査

Full-stack .Net 10 Clean Architecture (Microservices, Modular Monolith, Monolith), Blazor, Angular 21, React 19, Vue 3.5, BFF with YARP, NextJs 16, Domain-Driven Design, CQRS, SOLID, Asp.Net Core Identity Custom Storage, OpenID Connect, EF Core, OpenTelemetry, SignalR, Background Services, Health Checks, Rate Limiting, Clouds (Azure, AWS, GCP), ...

2.4K
Stars
83/100
信頼
カテゴリ: devops監査

Turn your PC, Mac, or Linux box into an AI server. LLM inference, chat UI, voice, agents, workflows, RAG, and image generation.

2.0K
Stars
78/100
信頼
カテゴリ: automation監査

Spoon is a metaprogramming library to analyze and transform Java source code. :spoon: is made with :heart:, :beers: and :sparkles:. It parses source files to build a well-designed AST with powerful analysis and transformation API.

1.9K
Stars
75/100
信頼
カテゴリ: development監査

可能是最深度的 AI 投研报告 Skill:九章个股深研 + 九章财报深度分析,脚本化 DCF/EPV 与可复算估值

234
Stars
75/100
信頼
カテゴリ: utility監査

Two Claude Skills that turn agents into AI film directors, providing cinematic dramaturgy and exact prompt syntax for major video/image models.

118
Stars
76/100
信頼
カテゴリ: design-creative監査

A self-learning skill layer for Claude Code that automatically distills, merges, updates, and prunes skills from real sessions.

413
Stars
75/100
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

A Claude Code custom skill for generating structured Chinese prompts for ByteDance's Seedance 2.0 AI video generation platform.

2.2K
Stars
72/100
信頼
カテゴリ: media監査

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

25K
Stars
77/100
信頼
カテゴリ: design-creative監査