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 →
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🔒 Is jev-eval-agent safe to install? View the security audit →
jev-eval-agent 🇺🇸 English · 🇧🇷 Leia em português A personal-assistant agent built with eve (Vercel), with 100 mocked tools, served through OpenRouter. The repository exists to answer one question: how many steps does the agent need to finish the same task when the LLM picks the tool itself vs. when Jev (TypeSafe's classifier) picks it? Everything lives on ; the mode is a single environment variable. Instructions, catalog, evals and UI are identical in both modes. How it works agent/ agent.ts model (OpenRouter); reasoning and providerOptions depend on MODE mode.ts reads AGENTMODE (llm-direct | jev-classifier) instructions.md shared system prompt instructions/mode.ts extra guidance per mode (session.started) lib/models.ts model presets (EVALMODEL) lib/catalog.ts 100 tools: name, description, zod schema and mock executor lib/world.ts deterministic mock "world" (calendar, contacts, flights, expenses…)
| Stars | 96 |
| Forks | 10 |
| Language | HTML |
| Category | AI Tool |
| Quality Score | 51.6792842223526/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-17 |
| Created | 2026-09-17 |
| Est. Tokens | ~18k |
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jev-eval-agent is an open-source ai tool by vinilana with 96 GitHub stars.
jev-eval-agent is primarily written in HTML.
You can find installation instructions and usage details in the jev-eval-agent GitHub repository at github.com/vinilana/jev-eval-agent. The project has 96 stars and 10 forks, indicating an active community.
The top alternatives to jev-eval-agent on Agent Skills Hub include SciToolAgent, redesigned-pancake, claude-code-voice-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: