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 Anil-matcha · Agent Tool · ★ 667
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-jev-by-typesafe safe to install? View the security audit →
Awesome Jev by TypeSafe ▶ Watch: Jev: 8 Wild Things People Already Built With TypeSafe's New AI Model An evidence-backed, practical collection of use cases, patterns, prompts, and starter code for Jev, TypeSafe AI’s first System One Model. Jev is built for software that needs a judgment, not a paragraph: Use it to classify, route, score, detect, rank, extract, verify, and gate automation. Keep the final control flow, thresholds, and side effects in code. This is an independent community collection. It is not an official TypeSafe AI repository. Product behavior, pric
| Stars | 667 |
| Forks | 120 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.7922463909099/100 |
| Last Updated | 2026-09-20 |
| Created | 2023-05-17 |
| Platforms | python |
| Est. Tokens | ~22k |
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awesome-jev-by-typesafe is Evidence-backed use cases, patterns, prompts, and starter code for TypeSafe Jev — a System One model for fast, typed, confidence-aware decisions in software.. It is categorized as a Agent Tool with 667 GitHub stars.
awesome-jev-by-typesafe is primarily written in Python. It covers topics such as agent-workflows, ai-api, ai-automation.
You can find installation instructions and usage details in the awesome-jev-by-typesafe GitHub repository at github.com/Anil-matcha/awesome-jev-by-typesafe. The project has 667 stars and 120 forks, indicating an active community.
awesome-jev-by-typesafe is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-jev-by-typesafe on Agent Skills Hub include awesome-jev, SkillCorpus, mira-OSS. 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: