hierarchical-topopt
hierarchical-topopt is a Physics, mathematics & simulation skill for AI agents, published by lamm-mit in the ScienceSkills repository on GitHub. Skillspace rates hierarchical-topopt C (78/100) and it passed the safety scan; the rating is based on static analysis only.
Run fast 2D SIMP topology optimization with flexible boundary conditions, lightweight STL exports, and optional STL turntable renderings. Use when asked to optimize cantilevers, beams, bridges, plates, tension/shear strips, custom support/load layouts, density fields, compliance minimization, profile/flat/multimaterial STL meshes, polished STL previews, or rotating GIFs of optimized geometry. Produces density plots/data, boundary-condition previews and resolved node/DOF JSON, optimization history, summary files, optional STL meshes, and optional STL render artifacts by running bundled Python scripts.
- Grade
- C
- Quality
- 78 / 100
- Safety
- Passed
- GitHub stars
- 35
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 96 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 1 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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