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 w3cj · Agent Tool · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jev-chat safe to install? View the security audit →
Jev Chat A chat-shaped command bar that calls real tools, without an LLM writing anything. Every turn, a classifier picks: what was asked, which tool to call, which value goes in each argument, whether to confirm first, and what kind of reply to give. Code does the rest: it calls the MCP servers and builds the reply from the tools' own data. Because no model ever writes the text, the assistant cannot invent a fact: every value on screen was either typed by the user or returned by a tool. An inspector pane shows the whole decision for any reply: the request, every question, every probability, and what the code did with the answers. Jev Chat What is Jev? Run it How it works Adding a tool Layout Notes FAQ References and resources Contributing What is Jev? Jev is a classifier from TypeSafe. You hand it some state and a set of named questions; it answers each one by choosing among options you supply. This app uses two of its question types: "Which of these tools fits?"
| Stars | 56 |
| Forks | 12 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62.4921176623285/100 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-18 |
| Platforms | node |
| Est. Tokens | ~21k |
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jev-chat is A tool calling chat bot built with Jev and no LLM.. It is categorized as a Agent Tool with 56 GitHub stars.
jev-chat is primarily written in TypeScript.
You can find installation instructions and usage details in the jev-chat GitHub repository at github.com/w3cj/jev-chat. The project has 56 stars and 12 forks, indicating an active community.
jev-chat is released under the MIT license, making it free to use and modify according to the license terms.
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.
Sources & who's responsible: