Geometry Generator
SkillMonitoring & opsGenerate parametric bioinspired ribbed membrane STL geometry via LLM-guided design. Takes a spec JSON (from StructureAnalyst/PropertyPredictor upstream artifacts), calls the LLM with a structured CAD prompt to produce design parameters, then builds a triangulated STL mesh in Python. Returns artifact JSON with stl_path, mesh stats, and the prompt used.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Geometry Generator skill
What this skill tells your AI
The instructions your AI receives, as published by lamm-mit/scienceclaw in skills/geometry-generator/SKILL.md and read by ahel’s review.
Generates parametric bioinspired hierarchical ribbed membrane STL geometry.
All design parameters flow from upstream artifacts (StructureAnalyst motifs + PropertyPredictor targets) — no hardcoded values.
Usage
# From upstream artifact spec file
python3 {baseDir}/scripts/stl_generator.py \
--spec '{"rib_spacing_mm":2.5,"thickness_mm":0.4,"aspect_ratio":3.0,"num_scales":2}' \
--output /tmp/membrane.stl
# From upstream artifact file
python3 {baseDir}/scripts/stl_generator.py \
--spec-file /path/to/structural_motifs.json \
--output /tmp/membrane.stl
Output JSON
{
"stl_path": "/path/to/membrane.stl",
"num_vertices": 1234,
"num_faces": 2468,
"bounding_box_mm": {"x": 20.0, "y": 20.0, "z": 1.2},
"primary_rib_count": 8,
"secondary_rib_count": 16,
"prompt_used": "...",
"design_params": {...}
}
STL Prompt
The LLM is called with the canonical bioinspired ribbed membrane prompt (see PROMPT.md). It returns structured design parameters as JSON. Python then constructs the mesh from those parameters.
Signals
- GitHub stars
- 242
- Forks
- 42
- Last commit
- Aug 2026
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geometry-generator- Source
- github.com/lamm-mit/scienceclaw