by FanBroWell · LLM Plugin · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 50 |
| Forks | 4 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 62/100 |
| Last Updated | 2026-11-04 |
| Created | 2026-06-01 |
| Platforms | claude-code, python |
| Est. Tokens | ~14k |
🔍 AI-paper-reviewer 在 reviewer 发现问题之前，先自己发现。 面向 NeurIPS、ICML、ICLR、AAAI、KDD 等顶会投稿场景的严格论文自审 prompt 工具箱。它帮助你在投稿前检查逻辑、claim、实验、引用、双盲匿名、可复现性和 rebuttal 风险。 🇨🇳 中文 · 🇬🇧 English 很多拒稿痛点并不是一个致命错误,而是一串本来可以提前修掉的问题:claim 过强、定位不清、数字不一致、引用缺失、写作含糊、双盲泄露。 它帮你提前抓什么 证据支撑不了的过强 claim reviewer 容易挑战的 、、 引用缺口、相关工作定位不足、疑似幻觉引用 Abstract / Table / Figure 里的数字不一致 投稿前的 reproducibility checklist 风险...
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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: