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 k-telux · Agent Tool · ★ 215
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpticalModeler safe to install? View the security audit →
OpticalModeler From 2D photonics schematics to physically auditable Blender optical tables. OpticalModeler is an evidence-first Agent Skill for reconstructing laboratory optical paths in Blender. It treats optical topology, real apertures, manufacturer CAD, fasteners, load paths, fiber routing, and artifact lineage as hard acceptance gates—not decorative details. Independent community project. Not affiliated with or endorsed by Thorlabs, Inc. Product names identify compatible hardware only. A rendered CAD assembly is not a mechanical, spectral, laser-safety, or experimental certification. Why OpticalModeler Centered apertures, splitter
| Stars | 215 |
| Forks | 2 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.3865275524602/100 |
| Last Updated | 2026-09-04 |
| Created | 2026-07-15 |
| Platforms | python |
| Est. Tokens | ~14k |
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OpticalModeler is Evidence-gated Agent Skill for reconstructing 2D photonics schematics as physically auditable Blender optical tables with CAD, beam-path, mechanics, and render proof.. It is categorized as a Agent Tool with 215 GitHub stars.
OpticalModeler is primarily written in Python. It covers topics such as agent-skills, blender, optical-design.
You can find installation instructions and usage details in the OpticalModeler GitHub repository at github.com/k-telux/OpticalModeler. The project has 215 stars and 2 forks, indicating an active community.
OpticalModeler is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpticalModeler on Agent Skills Hub include FigMirror, sim-cli, academic-figure-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: