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 xvchujin · Codex Skill · ★ 82
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is umat-skill safe to install? View the security audit →
Abaqus UMAT Skills 一套面向 Codex 的 Abaqus UMAT/VUMAT 专项 skills，覆盖本构机制路由、返回映射、张量接口、状态事务、损伤审计、木材连续损伤、空间正则化、切线验证和分层验收。 这是公开净化版。 仓库不包含书籍、论文全文、PDF、课程逐字材料、视频时间码、OCR 文本、第三方 Fortran 源码、附件、ODB/INP 工程包或内部研究语料。 项目目标 这组 skills 不是一份“万能 UMAT”，而是一套问题路由与审计工作流。每个 skill 只负责一个明确机制边界，避免把不同本构角色混装，例如： 不把 Hill48 等效应力直接塞进 J2 径向返回； 不把 Tsai-Wu 失效判据当成塑性返回残差； 不把 、单张云图或弹性 当作一致性证明； 不把不同 布局当成可直接重启的同一 ABI； 不在局部求解失败后提交半更新的应力、状态或能量。 25 个 skills tsai-wu-damage-sta
| Stars | 82 |
| Forks | 0 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 48.9124206605046/100 |
| Last Updated | 2026-08-22 |
| Created | 2026-08-17 |
| Platforms | codex, python |
| Est. Tokens | ~9k |
Looking for a umat-skill alternative? If you're comparing umat-skill with other codex skill tools, these 1 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
A Codex/Claude skill that maintains per-repo operating theory documents.
Explore other popular codex skill tools:
umat-skill is Abaqus UMAT Codex skills for constitutive modeling, return mapping, damage, state contracts, tangent auditing, and hierarchical validation.. It is categorized as a Codex Skill with 82 GitHub stars.
umat-skill is primarily written in Python.
You can find installation instructions and usage details in the umat-skill GitHub repository at github.com/xvchujin/umat-skill. The project has 82 stars and 0 forks, indicating an active community.
The top alternatives to umat-skill on Agent Skills Hub include theorist. 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: