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git clone https://github.com/RenaGao/jev-dataops.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 | 65 |
| Forks | 8 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 65/100 |
| Last Updated | 2026-10-08 |
| Created | 2026-09-21 |
| Platforms | python |
| Est. Tokens | ~21k |
JEV DataOps A traceable pipeline for general and domain-specific data: upload → screen → evaluate data → train a model → evaluate the result. Try the public browser demo — no installation, API key, or GPU required. Click Use example → Start workflow, then inspect screening decisions, measured evaluation, and downloadable reports.
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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.
Sources & who's responsible: