skills/geometry-generator/SKILL.md
Generate 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.
npx skillsauth add lamm-mit/scienceclaw geometry-generatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Generates parametric bioinspired hierarchical ribbed membrane STL geometry.
All design parameters flow from upstream artifacts (StructureAnalyst motifs + PropertyPredictor targets) — no hardcoded values.
# 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
{
"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": {...}
}
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.
tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.