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geometry-generator

Generate parametric bioinspired ribbed membrane STL geometry via LLM-guided design.

Skill by lamm-mit

IndexedApache-2.0★ 250
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/lamm-mit-geometry-generator

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About

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.

Use cases: design, ml, agent-building

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Secrets
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Install scripts
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Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/geometry-generator

What's inside

Skill name
geometry-generator
Description
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.
License
Apache-2.0
Metadata
category: materials-design · requires: numpy, scipy, core/llm_client
Instructions
43 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (4)

  • PROMPT.md
  • SKILL.md
  • stl_generator.cpython-313.pyc · scripts/__pycache__/
  • stl_generator.py · scripts/

SKILL.md (first lines)

# Geometry Generator

Generates parametric bioinspired hierarchical ribbed membrane STL geometry.

All design parameters flow from upstream artifacts (StructureAnalyst motifs +
PropertyPredictor targets) — no hardcoded values.

## Usage

```bash
# 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

```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.

From lamm-mit/scienceclaw, Apache-2.0.

More from lamm-mit/scienceclaw

Signals

Stars
250
Forks
47
Open issues
1
Last commit (this folder)
Mar 14, 2026 (7 months ago)
Last release
Jun 23, 2026 (4 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
Repository
github.com/lamm-mit/scienceclaw
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

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Cite this page

geometry-generator skill. Agent Mart. https://agentmart.dev/skills/lamm-mit-geometry-generator (last updated 2026-10-24)