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 IAmUnbounded · AI Tool · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is save-token-jev-clean safe to install? View the security audit →
save-token-jev Portable, Jev-guided context compaction for coding agents. Instead of asking another LLM to rewrite old context into a lossy summary, asks Jev which tool calls and results still matter. User and assistant text is kept verbatim. A tool call can be kept with its full result, kept with a bounded result, or removed together with its result. The algorithm is implemented behind a normalized transcript model and host adapters so the same compaction policy works across multiple coding-agent runtimes. Supported hosts Codex currently does not expose a command-hook response that replaces its compacted transcript. Its inte
| Stars | 56 |
| Forks | 10 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 61.7748885442198/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-18 |
| Platforms | node |
| Est. Tokens | ~5k |
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save-token-jev-clean is an open-source ai tool by IAmUnbounded with 56 GitHub stars.
save-token-jev-clean is primarily written in TypeScript.
You can find installation instructions and usage details in the save-token-jev-clean GitHub repository at github.com/IAmUnbounded/save-token-jev-clean. The project has 56 stars and 10 forks, indicating an active community.
save-token-jev-clean is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to save-token-jev-clean on Agent Skills Hub include redesigned-pancake, claude-code-voice-skill, maui-skills. 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: