by Chenyu-Wang567 · LLM Plugin · ★ 142
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/MLLM-Tool/MLLM-Tool.gitTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 142 |
| Forks | 5 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 71/100 |
| Open Issues | 5 |
| Last Updated | 2025-10-10 |
| Created | 2024-01-08 |
| Platforms | python |
| Est. Tokens | ~193k |
MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning Chenyu Wang, Weixin Luo, Qianyu Chen, Haonan Mai, Jindi Guo, Sixun Dong, Xiaohua (Michael) Xuan, Zhengxin Li, Lin Ma, Shenghua Gao. ShanghaiTech University && Meituan && UniDT This repository hosts the code, data and model weight of MLLM-Tool, the first tool agent MLLM that has the ability to perceive visual- and auditory- input...
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: