CAD Skills vs FigureLabs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CAD Skills and FigureLabs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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CAD Skills
earthtojake
Open-source library of agent skills for CAD, robotics, fabrication, and simulation — generate STEP/STL, URDF/SRDF/SDF, DXF, and slicer G-code from prompts.
Key features
- Prompt-to-CAD Generation: The CAD skill creates and edits parametric CAD models from natural language or image requests, exporting to STEP, STL, 3MF, and GLB.
- URDF Robot Description: Writes robot structure files with links, joints, limits, inertials, and meshes ready for ROS-based stacks.
- SRDF for MoveIt2: Adds planning groups, end effectors, named poses, and collision rules on top of a URDF for MoveIt2 planning.
- SDF Simulation Worlds: Creates simulator models and worlds with frames, physics, sensors, and lights.
- 2D DXF Drawings: Produces cut-ready DXF profiles, templates, gaskets, and layouts from Python or CAD geometry.
- G-code Slicing: Slices supported mesh files into validated, printer-profiled FDM G-code using real slicer CLIs.
- Bambu Lab Print Jobs: Dry runs, uploads, and cautiously starts local Bambu Lab prints from validated G-code.
- CAD Viewer Previews: Local browser previews for CAD, G-code, URDF, and other robot files for fast agent iteration.
Best for
- Agentic Mechanical Design: Let a coding agent iterate on brackets, flanges, and enclosures directly from natural-language specs.
- Robot Description Authoring: Generate URDF/SRDF/SDF files for new robot arms or mobile bases without hand-writing XML.
- Rapid Prototyping: Take a CAD model to sliced, printer-ready G-code and kick off a Bambu Lab print from within an agent workflow.
- Custom Part Fabrication: Produce DXF/STEP files pre-checked for SendCutSend, then order laser-cut or CNC parts.
- Simulation Setup: Author SDF worlds for physics simulators with the right frames, sensors, and lighting for robotics research.
- Off-the-Shelf Sourcing: Use step.parts to pull ready-made STEP models for common hardware like screws, bearings, and connectors.
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FigureLabs
FigureLabs
AI agent that creates publication-ready scientific figures via text-to-figure, image-to-figure, and vectorization.
Key features
- Text-to-Figure Generation: Creates complete, composed scientific figures from plain-text descriptions, allowing users to specify panels, annotations, and figure layout that the agent renders automatically.
- Image-to-Figure Conversion: Transforms input images (e.g., plots, microscopy snapshots, schematics) into polished figure components suited for publication, preserving scientific detail while improving presentation.
- Vectorization and Editable Output: Converts raster graphics into vector representations so figures are editable and scalable for high-resolution publication needs.
- Publication-Ready Styling: Applies formatting and styling conventions appropriate for academic journals, producing high-resolution outputs that reduce manual rework before submission.
- Rapid Iteration: Generates and refines figures in seconds, enabling fast prototyping and repeated adjustments during manuscript or presentation development.
- Precision Preservation: Focuses on preserving underlying data clarity and scientific details while enhancing visual clarity and label legibility for reproducible visuals.
- Text-to-figure generation from natural-language prompts
- Image-to-figure conversion (convert raster inputs into cleaned, publication-ready figures)
- Vectorization of raster graphics to vector formats (SVG)
- Fast generation workflow (seconds-scale) for rapid iteration
- Outputs optimized for publication (high-resolution and editable vector formats)
Best for
- Preparing manuscript figures for journal submission: generate composed, publication-ready multi-panel figures from descriptions and source images to accelerate paper submission.
- Converting lab outputs into editable graphics: turn raster plots or microscope images into vectorized, editable figures for revision and scaling without quality loss.
- Rapid prototyping of visual results: create multiple figure variants quickly to test layouts, annotations, and styles during manuscript drafting or poster design.
- Recreating figures from text or notes: produce visual representations of experimental setups, workflows, or conceptual diagrams from written descriptions for methods or review articles.
- Improving figure consistency across a manuscript: standardize styling, labels, and panel layouts across multiple figures to meet journal formatting requirements and improve readability.
- Create publication figures for manuscripts, posters, and presentations
- Convert hand-drawn or raster diagrams into editable vector figures
- Rapidly prototype visualizations from experimental descriptions
- Produce consistent, publication-ready figure sets with minimal manual redrawing