No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by onejune2018 · Agent Tool · ★ 654
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-LLM-Eval safe to install? View the security audit →
Awesome LLM Eval English | 中文 Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on Large Language Models and exploring the boundaries and limits of Generative AI. The is the official project of our survey: Beyond Benchmark: LLMs Evaluation with an Anthropomorphic and Value-oriented Roadmap. NOTE: As we cannot update the arXiv paper in real time, please refer to this repo for the latest updates and the paper may be updated later. We also welcome any pull request or issues to help us improve this work. Your contributions will be acknowledged in acknowledgements. If you find our survey useful, please kindly cite our paper: Table of Contents News [Tools]
| Stars | 654 |
| Forks | 83 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 58.6727772105924/100 |
| Open Issues | 46 |
| Last Updated | 2025-11-24 |
| Created | 2023-04-26 |
| Platforms | aws |
| Est. Tokens | ~1132k |
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Awesome-LLM-Eval is 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.. It is categorized as a Agent Tool with 654 GitHub stars.
You can find installation instructions and usage details in the Awesome-LLM-Eval GitHub repository at github.com/onejune2018/Awesome-LLM-Eval. The project has 654 stars and 83 forks, indicating an active community.
Awesome-LLM-Eval is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Awesome-LLM-Eval on Agent Skills Hub include spacy-llm, cai, sre. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: