NanoJev: A nano replica of Jev: parallel decisions, dynamic candidates, and an end-to-end training pipeline

by TianyuCodings · Agent Tool · ★ 2.6k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install NanoJev

git clone https://github.com/TianyuCodings/NanoJev.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 60/100

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.

Quick Facts

Stars2,578
Forks265
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score60/100
Open Issues9
Last Updated2026-10-16
Created2026-09-17
Platformspython
Est. Tokens~16k

About NanoJev

NanoJev — A nano replica of Jev English | 简体中文 A 0.6B parallel decision model. States and questions in, complete probability distributions out—with zero output-token decoding. Model · Dataset Open the live side-by-side demo → Three models, one game Download the maze video (MP4) · 27 seconds · 1440 × 1120 · 30 fps Play Snake · Explore the 50×50 maze · Recorded sources and replay checks The...

Compatible Skills

These tools work well together with NanoJev for enhanced workflows:

  • agent-link — semantic(0.48)+complementary+same_lang+similar_pop+shared_platform (67%)
  • nautex — semantic(0.47)+complementary+same_lang+similar_pop+shared_platform (66%)
  • openjev-sglang — semantic(0.21)+complementary+shared_fw(huggingface)+same_lang+similar_pop+shared_platform (65%)
  • mini-jev — semantic(0.16)+complementary+shared_fw(huggingface)+same_lang+similar_pop+shared_platform (63%)

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How this security grade is produced

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:

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