Skill 디렉토리

AI Agent를 위한 재사용 가능한 Skill을 찾으세요.

작업으로 실제 GitHub Skill을 검색하고 사용 전에 Stars, 신뢰, 감사, 카테고리, 설치 경로를 확인하세요.

모든 추천은 리포지토리, 감사, 설치 경로와 명확하게 연결됩니다.

검색 결과: benchmarks

영문 디렉토리

Benchmarking PDF libraries

337
Stars
62/100
신뢰
카테고리: document-processing감사
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
신뢰
카테고리: automation감사

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

23K
Stars
82/100
신뢰
카테고리: coding-agents감사

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

4.2K
Stars
85/100
신뢰
카테고리: robotics-iot감사

access to david ondrej's personal agent skills

2.7K
Stars
76/100
신뢰
카테고리: utility감사

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

2.5K
Stars
84/100
신뢰
카테고리: rag-knowledge감사

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

3.0K
Stars
76/100
신뢰
카테고리: research감사

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
신뢰
카테고리: marketing-growth감사

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
신뢰
카테고리: media-automation감사

Reference implementations of MLPerf® training benchmarks

1.8K
Stars
76/100
신뢰
카테고리: ml-automation감사

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

1.1K
Stars
80/100
신뢰
카테고리: agent-skills감사

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
신뢰
카테고리: research감사