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 zhangxjohn · Agent Tool · ★ 169
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LLM-Agent-Benchmark-List safe to install? View the security audit →
LLM-Agent-Benchmark-List 🤗We greatly appreciate any contributions via PRs, issues, emails, or other methods. ⏳ Continuous update... :book: Introduction In the swiftly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as a pivotal cornerstone, revolutionizing how we interact with and harness the power of natural language processing. However, as LLMs gain widespread application in both research and industry sectors, the imperative shifts towards evaluating their efficacy rather than perpetuating a cycle of unbridled performance iterations. This paradigm shift raises critical questions: i) what to evaluate? ii) where to evaluate? iii)How to evaluate? Diverse research endeavors have proposed varying interpretations and methodologies in response to these queries. The aim of this work is to methodically review and organize benchmarks that are both LLMs and agent-powered, thereby providing a streamlined resource for those journeying towards Artificial General Intelligence (AGI). :dizzy: List Survey [2023/07] A Survey on Evaluation of Large Language Models. Yupeng Chang ( Jilin University) et al. arXiv.
| Stars | 169 |
| Forks | 12 |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 53.6349017474777/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-19 |
| Created | 2024-01-29 |
| Est. Tokens | ~19k |
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LLM-Agent-Benchmark-List is A banchmark list for evaluation of large language models.. It is categorized as a Agent Tool with 169 GitHub stars.
You can find installation instructions and usage details in the LLM-Agent-Benchmark-List GitHub repository at github.com/zhangxjohn/LLM-Agent-Benchmark-List. The project has 169 stars and 12 forks, indicating an active community.
LLM-Agent-Benchmark-List is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to LLM-Agent-Benchmark-List on Agent Skills Hub include bigcodebench, LLM-Tool-Survey, OpenRCA. 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: