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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jev-lint A linter for the things a linter could never check: whether a function does what its name says, whether a comment is still true, whether a test verifies what it claims. What it reviews Conventional lint decides things a parser can decide. The conventions teams actually argue about in review are not like that: A name that drifted from its implementation. that also saves the cart; bound to a string; a test called rejects an expired token that never passes one. A comment that became a lie. A doc comment separated from its function by a later refactor, still claiming a return the function no longer makes. Design consistency. Whether this module is named for what it contains, whether blocks hide failures, whether a has a timeout unl
| Stars | 115 |
| Forks | 1 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 62.6494135492042/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-02 |
| Created | 2026-09-19 |
| Platforms | node |
| Est. Tokens | ~18k |
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jev-lint is lint text in code by jev scorerer. It is categorized as a AI Tool with 115 GitHub stars.
jev-lint is primarily written in TypeScript. It covers topics such as ai, jev.
You can find installation instructions and usage details in the jev-lint GitHub repository at github.com/mizchi/jev-lint. The project has 115 stars and 1 forks, indicating an active community.
jev-lint is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jev-lint on Agent Skills Hub include phi, neurolink, awesome-typesafe. 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: