Openclaw Skills converts dimensioned images into confirmation-ready SVG blueprints and accurate STL models for fabrication.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image-to-3d-model
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install image-to-3d-model using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Image to 3D Part STL Builder?
Openclaw Skills for the Image to 3D Part STL Builder helps an AI coding agent transform user-provided images, sketches, or parts with dimensions into high-precision 2D SVG layouts and 3D STL print-ready models.
It is designed for iterative engineering workflows: first extract geometry from the image, then generate a dimensioned SVG for user confirmation, and only after approval rewrite the generation logic in generate.py to produce the final STL. Openclaw Skills makes this process reliable for 3D printing, plotting, and light machining by combining visual interpretation, mathematical contour reconstruction, and controlled model generation.
Image to 3D Part STL Builder Use Cases
- Convert annotated part photos or sketches into a clean 2D SVG plan.
- Generate STL files from images with known dimensions for 3D printing.
- Rebuild contours for mechanical parts, brackets, panels, and custom enclosures.
- Create vector plans for laser cutting, CNC plotting, or machining references.
- Iterate on geometry when test prints reveal tolerance, wall thickness, or fit issues.
- Use Openclaw Skills when the workflow requires image-to-geometry conversion with user confirmation before fabrication.
How Image to 3D Part STL Builder Works
- Inspect the uploaded image and extract geometric features such as widths, radii, diameters, flats, lengths, and hole locations.
- Compute precise coordinates for the contour and any internal cutouts using the visible dimensions and any supporting text notes.
- Generate a high-precision SVG draft with red dimension lines and labels, then stop and ask the user to confirm the contour.
- Revise the SVG if the user requests changes until the blueprint matches expectations.
- After explicit confirmation, read the
src/generate.pytemplate and rewrite the contour logic insidegenerate_3d_model. - Set up a Python virtual environment, install dependencies, and run the script to generate the STL output.
- Deliver the generated file path to the user and repeat the loop for fit, tolerance, and thickness refinements if needed.
Image to 3D Part STL Builder Setup
- Ensure the working directory is available and the image plus any dimension notes have been provided.
- Create and activate a Python virtual environment in the working directory.
- Install the skill dependencies from
requirements.txt. - Run the generation script from the skill template after the SVG has been confirmed.
python -m venv venv
./venv/Scripts/pip install -r <path_to_skill>/requirements.txt
./venv/Scripts/python <path_to_skill>/src/generate.py --output part.stl --thickness 2.0
- Review the generated SVG in a browser or CAD/viewer before approving STL creation.
- If dimensions or fit are incorrect, update the SVG and regeneration logic, then rerun the script.
- Confirm the final STL file path once generation succeeds.
Image to 3D Part STL Builder Data Schema & Taxonomy
Openclaw Skills for this builder organizes its files into a compact template-driven workflow:
| Path | Purpose | Notes |
|---|---|---|
requirements.txt |
Python dependencies for geometry and mesh generation | Includes numpy, trimesh, shapely, and mapbox_earcut |
src/generate.py |
STL generation template | The agent rewrites the contour generation logic based on confirmed image analysis |
SKILL.md |
Workflow and usage instructions | Defines the trigger conditions, confirmation loop, and regeneration steps |
Generated artifacts
output.svg: Confirmation-stage 2D vector blueprint with red dimension annotations.part.stl: Final 3D printable mesh output.- Optional regenerated outputs during iteration when tolerances or geometry change.
Geometry metadata taxonomy
- Outer contour: The primary boundary point set used to form the part outline.
- Inner holes: Internal cutouts or voids represented as hole point sets.
- Dimensional constraints: Radii, diameters, widths, lengths, flats, and thickness values extracted from the image or user text.
- Tolerances: Adjustments applied during refinement for print fit or machining clearance.
- Validation state: Whether the SVG blueprint has been user-confirmed before STL generation.
Image to 3D Part STL Builder Advanced Features
- Interactive SVG confirmation loop before any STL generation to reduce fabrication errors.
- Template-based code rewriting in
src/generate.pyfor direct secondary development. - Supports both image-derived measurements and text-supplied dimensional constraints.
- Produces dual outputs for different manufacturing stages: SVG for review and STL for printing.
- Iterative tuning workflow for wall thickness, hole clearance, and fit corrections.
- Openclaw Skills can be adapted for engineering plotting, part replication, and custom fabrication pipelines.
SKILL.md
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