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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JevForge An end-to-end toolkit for synthesizing decision data, training calibrated candidate scorers, evaluating them, and serving Jev-compatible inference for interactive web decisions. Data synthesis · training + calibration · evaluation · Jev-compatible serving Interactive demo · HF model · HF dataset · Dataset format · Chinese · Design · RLCD results JevForge turns structured decisions into one reproducible pipeline. Give it a state, a question, and a candidate set; it builds training records, trains and calibrates a scorer, evaluates fixed splits, and serves a complete probability distribution through a Jev-compatible API. , , and ordered questions all use the same decision path. Its first proving ground is the interaction loop at the heart of Jev-style systems: choosing the next click, navigation target, form control, route, or escalation from the elements currently available on a page. Results Results use fixed,
| Stars | 127 |
| Forks | 13 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 63.333022491971/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-23 |
| Created | 2026-09-19 |
| Platforms | python |
| Est. Tokens | ~15k |
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jev-forge is An open training and inference stack for Jev-style decision models. Train models to score dynamic candidate branches from a shared prefix, with support for high-cardinality choice, calibration, and f. It is categorized as a AI Tool with 127 GitHub stars.
jev-forge is primarily written in Python.
You can find installation instructions and usage details in the jev-forge GitHub repository at github.com/zwliJay/jev-forge. The project has 127 stars and 13 forks, indicating an active community.
The top alternatives to jev-forge 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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