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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semgrep — grep by meaning 日本語版はこちら A grep that finds lines by what they mean, not by regular expressions. Matching is done by Jev, the System One model from TypeSafe AI. Jev does not generate text. It answers typed questions with probabilities, so for every line semgrep asks "does this line match the meaning network failure?", gets a probability back, and applies a threshold. Zero dependencies. One file, Node.js 20.12+ and . Fast. 30 lines go into one request, requests run 8 at a time. A 210-line file finishes in under a second. Meanings combine with AND / OR / NOT. Language-agnostic. The meaning and the text can each be in any language. A Japanese meaning finds French, Russian, Chinese and Korean lines alike. No translation step, same speed, same cost. Search across languages The meaning and the text do not have to share a language. Jev compares concepts, not words, so one query finds matching lines in every language the file contains. An English meaning finds Japanese lines.
| Stars | 107 |
| Forks | 4 |
| Language | JavaScript |
| Category | AI Tool |
| Quality Score | 63.1766448659005/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-19 |
| Platforms | node |
| Est. Tokens | ~17k |
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jev-semgrep is grep by meaning, across languages. TypeSafe Jev scores every line against a meaning; combine meanings with AND/OR/NOT. 意味で探す grep。日本語で英語を、英語で日本語を検索できる. It is categorized as a AI Tool with 107 GitHub stars.
jev-semgrep is primarily written in JavaScript.
You can find installation instructions and usage details in the jev-semgrep GitHub repository at github.com/uehaj/jev-semgrep. The project has 107 stars and 4 forks, indicating an active community.
The top alternatives to jev-semgrep 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.
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