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 xianminx · Agent Tool · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mooc-cs294-llm-agents safe to install? View the security audit →
CS294/194-196 Large Language Model Agents Coursework for Mooc Large Language Model Agents Check the course website for more information. Quizes Labs Lab 1 Lab 2 Lab 3
| Stars | 50 |
| Forks | 13 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 34.5098318634976/100 |
| Last Updated | 2024-12-20 |
| Created | 2024-10-21 |
| Platforms | python |
| Est. Tokens | ~4977k |
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mooc-cs294-llm-agents is CS294/194-196 Large Language Model Agents. It is categorized as a Agent Tool with 50 GitHub stars.
mooc-cs294-llm-agents is primarily written in Python.
You can find installation instructions and usage details in the mooc-cs294-llm-agents GitHub repository at github.com/xianminx/mooc-cs294-llm-agents. The project has 50 stars and 13 forks, indicating an active community.
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.
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