by receptron · LLM Plugin · ★ 986
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npm install @receptron/layaTaken from the project's README on GitHub. Check the security audit before running it.
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 | 986 |
| Forks | 81 |
| Language | TypeScript |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 72/100 |
| Open Issues | 5 |
| Last Updated | 2026-10-17 |
| Created | 2026-09-19 |
| Platforms | node |
| Est. Tokens | ~15k |
@receptron/laya Run Laya — the open-source, Jev-compatible System 1 decision model by Convai Innovations — from Node.js / TypeScript. Laya does not generate text. You hand it a state (a ticket, an email, a JSON object) and typed questions, and it returns every answer with calibrated probabilities in one forward pass: — pick one option, with a probability per option — an expected level on an...
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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: