by AustinAWay · AI Tool · ★ 79
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git clone https://github.com/AustinAWay/Working-Memory-Jev.gitTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 79 |
| Forks | 12 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 53/100 |
| Last Updated | 2026-09-21 |
| Created | 2026-09-18 |
| Platforms | python |
| Est. Tokens | ~15k |
Working Memory Jev · Passage Passage helps educators inspect where instructional text may ask a learner to hold too many ideas or relationships at once. It runs on localhost and uses the real Jev API to propose active groups, source connections, and changes in demand as the passage unfolds. This is an experimental teaching aid.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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