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 learningmatter-mit · Codex Skill · ★ 164
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AtomisticSkills safe to install? View the security audit →
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. The framework integrates state-of-the-art Machine Learning Interatomic Potentials (MLIPs), DFT calculations, generative AI, database APIs, and advanced simulation methods through the Model Context Protocol (MCP) tools and Skills, making advanced materials research accessible to AI copilots like Google Antigravity, Cursor, Claude Code, and OpenAI Codex. 🌐 Documentation Website | 📄 Preprint 🎬 Video Demo: Using AtomisticSkills in Google Antigravity — Watch on YouTube [](https://www
| Stars | 164 |
| Forks | 24 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 63.4426954129754/100 |
| Last Updated | 2026-09-14 |
| Created | 2026-01-08 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
These tools work well together with AtomisticSkills for enhanced workflows:
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AtomisticSkills is Intergrating Atomistic Skills into Agentic IDEs (Cursor, Claude Code, Google Antigravity, OpenClaw, etc). It is categorized as a Codex Skill with 164 GitHub stars.
AtomisticSkills is primarily written in Python.
You can find installation instructions and usage details in the AtomisticSkills GitHub repository at github.com/learningmatter-mit/AtomisticSkills. The project has 164 stars and 24 forks, indicating an active community.
AtomisticSkills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AtomisticSkills on Agent Skills Hub include ai-maestro, upskill, skills. 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: