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: leaderboard

英文目录

SpeechIO Leaderboard: a large, robust, comprehensive, benchmarking platform for Automatic Speech Recognition.

545
Stars
63/100
信任
分类: media-automation审计

🛰️ A CLI tool for tracking token usage from OpenCode, Claude Code, 🦞OpenClaw (Clawdbot/Moltbot), Pi, Codex, Gemini, Cursor, AmpCode, Factory Droid, Kimi, and more! • 🏅Global Leaderboard + 2D/3D Contributions Graph

3.9K
Stars
74/100
信任
分类: development审计

35 production-grade agentic AI architectures (Reflexion, LATS, GraphRAG, MemGPT, Voyager, BrowserAgent, ...) — a Python library and runnable textbook with multi-provider LLM support and a 17-task benchmark leaderboard.

3.7K
Stars
85/100
信任
分类: agent-frameworks审计

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

51K
Stars
83/100
信任
分类: research审计

Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!

2.1K
Stars
73/100
信任
分类: ml-automation审计

A cross-platform AI agent skill that performs standardized health checkups with dual-axis scoring and public leaderboard integration.

95
Stars
67/100
信任
分类: utility审计

Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard

571
Stars
71/100
信任
分类: rag-knowledge审计

🏆 The AI coding usage leaderboard — Claude Code, Codex, Gemini CLI & more. Real costs and tokens from ccusage data. Submit with: npx viberank-cli

102
Stars
68/100
信任
分类: coding-agents审计

Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表,主要面向基础大模型评测,旨在探求生成式AI的技术边界.

642
Stars
68/100
信任
分类: rag-knowledge审计

A joint community effort to create one central leaderboard for LLMs.

306
Stars
62/100
信任
分类: ml-automation审计

READ this skill when designing or planning any game system architecture — including combat, skills, AI, UI, multiplayer, narrative, or scene systems. Contains paradigm selection guides (DDD / Data-Driven / Prototype), system-specific design references, and mixing strategies. Works as a domain knowledge plugin alongside workflow skills (OpenSpec, SpecKit) or plan mode of an agent.

57
Stars
57/100
信任
分类: research审计

Cross-silo Federated Learning playground in Python. Discover 7 real-world federated datasets to test your new FL strategies and try to beat the leaderboard.

239
Stars
62/100
信任
分类: geo-science审计