by learningmatter-mit · Codex Skill · ★ 173
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
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 | 173 |
| Forks | 27 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 63/100 |
| Last Updated | 2026-09-28 |
| Created | 2026-01-08 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~16k |
AtomisticSkills Overview AtomisticSkills is a composable framework for AI-driven atomistic materials research. Built on the hierarchical decomposition of complex scientific tasks into Workflows → Skills → Tools, it enables coding AI agents to autonomously conduct multi-stage materials, chemistry, and drug discovery research by combining modular, reusable capabilities.
These tools work well together with AtomisticSkills for enhanced workflows:
Looking for a AtomisticSkills alternative? If you're comparing AtomisticSkills with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Набор навыков для AI(ИИ)-агентов (Claude Code, Cursor, Codex и др.), помогающий охватить полный цикл разработк
Generate and evaluate agent skills for code agents like Claude Code, Open Code, OpenAI Codex
开源了Codex Skills等集合，大部分是自己实际需要搞得，需要的自取。欢迎star
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Claude Code / Codex skill: reverse engineer a product & technical spec from a git branch
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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: