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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. Full audit →
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 | 98 |
| Forks | 3 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 55/100 |
| Last Updated | 2026-09-21 |
| Created | 2026-09-21 |
| Platforms | python |
| Est. Tokens | ~6k |
Qwen Choice 简体中文 | English 基于 Qwen3.5-4B 的本地视觉判断工具。输入一张图片、一个问题和一组选项，通过一次模型前向计算，直接返回选项及其概率分布。 单卡 RTX A6000 实测：完整请求中位数 169 ms，P95 为 174 ms。 使用原始 BF16 权重，关闭思考，无训练或量化。 切换其余Qwen模型直接可以按这个逻辑加速。 示例 只回答一个问题：图片中的客户消息属于哪类客服问题？ 选项为 （账单）、（技术）、（销售）和 （其他）。 图片中的客户反映重复扣款，模型返回 。消息和图片均为虚构示例，问题与选项定义见 supportticket.json。 性能 以下为启用视觉与语言模型编译后的测量结果，日期为 2026-09-21。 测量条件：单卡 RTX A6000、BF16、batch=1；一张 1280×720...
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SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
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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: