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: benchmarks

英語版ディレクトリ

Benchmarking PDF libraries

337
Stars
62/100
信頼
カテゴリ: document-processing監査
Cua88

Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).

21K
Stars
88/100
信頼
カテゴリ: automation監査

#1 Persistent memory for AI coding agents based on real-world benchmarks

23K
Stars
82/100
信頼
カテゴリ: coding-agents監査

Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks

4.2K
Stars
85/100
信頼
カテゴリ: robotics-iot監査

access to david ondrej's personal agent skills

2.7K
Stars
76/100
信頼
カテゴリ: utility監査

Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.

2.5K
Stars
84/100
信頼
カテゴリ: rag-knowledge監査

🏆 Top-1 on 5+ benchmarks | Web UI | Supports MiroThinker, Claude, Kimi, OpenAI

3.0K
Stars
76/100
信頼
カテゴリ: research監査

A Claude Code skill that diagnoses social media accounts and analyzes viral content across Chinese platforms (Xiaohongshu, Douyin, Kuaishou, etc.), providing benchmarks, breakdowns, and copywriting drafts.

174
Stars
77/100
信頼
カテゴリ: marketing-growth監査

Open-source industrial-grade ASR models supporting Mandarin, Chinese dialects and English, achieving a new SOTA on public Mandarin ASR benchmarks, while also offering outstanding singing lyrics recognition capability.

1.9K
Stars
83/100
信頼
カテゴリ: media-automation監査

Reference implementations of MLPerf® training benchmarks

1.8K
Stars
76/100
信頼
カテゴリ: ml-automation監査

An Agent Skill helping you to optimize Xcode incremental and clean builds by running benchmarks and optimizing build settings.

1.1K
Stars
80/100
信頼
カテゴリ: agent-skills監査

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

34K
Stars
80/100
信頼
カテゴリ: research監査