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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 | 144 |
| Forks | 20 |
| Language | JavaScript |
| Category | AI Tool |
| Quality Score | 55/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-20 |
| Platforms | node |
| Est. Tokens | ~4k |
大声读 · ReadAloud 对着屏幕念一段英文，念完就知道哪个词念错了。 英语流利说那一套，一个开源的实时 ASR 模型加一个开源的判词模型就能平替 —— 而且可以整套跑在你自己的显卡上，不联网、不上传、不按分钟计费。 念的时候字是一个个亮起来的，念错的当场标红，念完点一下出总分。 一台 12GB 的卡就够。 两个模型，各干各的 R2T2 负责听。 它是真流式，最低 160ms 的步长往外吐词，还有个叫 stable prefix 的机制：已经提交的文本只增不改。传统流式 ASR 会来回改词， 变成 再变回来 —— 给人看字幕还行，可下游要是 接了另一个模型，它就得跟着反复撤销自己刚做的判断。正因为有这一层，屏幕上划过的 线不会闪、判过的分不用重判，漏读多读也能直接拿识别结果算。 Jev 负责判。 它只在你事先声明好的空间里回答：是否、选一个、1–5 打分，...
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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: