技能目录

为 AI Agent 发现可复用技能。

按任务搜索真实的 GitHub 技能,并在使用前查看 Stars、信任、审计、分类和安装路径。

每个推荐都保留与其仓库、审计和安装路径的明确关联。

搜索结果: benchmarks

英文目录

Benchmarking PDF libraries

337
Stars
62/100
信任
分类: document-processing审计
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
信任
分类: automation审计

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

23K
Stars
82/100
信任
分类: coding-agents审计

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

4.2K
Stars
85/100
信任
分类: robotics-iot审计

access to david ondrej's personal agent skills

2.7K
Stars
76/100
信任
分类: utility审计

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

2.5K
Stars
84/100
信任
分类: rag-knowledge审计

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

3.0K
Stars
76/100
信任
分类: research审计

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
信任
分类: marketing-growth审计

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
信任
分类: media-automation审计

Reference implementations of MLPerf® training benchmarks

1.8K
Stars
76/100
信任
分类: ml-automation审计

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

1.1K
Stars
80/100
信任
分类: agent-skills审计

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
信任
分类: research审计