earthtojake / text-to-cad

· Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Intermediate, medium

Summary

CAD Skills, a library by earthtojake, provides agent capabilities for generating, inspecting, sourcing, slicing, and managing CAD and robot-description artifacts from local project files. It supports a wide array of formats including STEP, STL, 3MF, GLB, DXF, URDF, SRDF, and SDF. Key functionalities include creating and editing CAD models from text or image prompts, local browser previews via CAD Viewer, and sourcing off-the-shelf STEP parts. The library also facilitates generating 2D DXF drawings, writing robot structure files (URDF, SRDF), and creating simulator models (SDF). For manufacturing, it offers G-code slicing for FDM printers, pre-upload checks for SendCutSend, and direct print job management for Bambu Lab. Installation is available through the Skills CLI or as plugins for Codex and Claude Code. Benchmarks showcase its ability to generate complex designs such as a planetary gear stage and a spiral staircase.

Key takeaway

For AI Engineers or Robotics Engineers integrating generative AI into hardware design, CAD Skills offers a robust framework to automate complex tasks. You can streamline workflows by enabling agents to generate CAD models from natural language, create robot description files, and prepare designs for fabrication. This library allows you to accelerate prototyping cycles and reduce manual intervention in design-to-manufacturing processes, enhancing efficiency across your development pipeline.

Key insights

CAD Skills provides a comprehensive agent-based toolkit for automated design, fabrication, and simulation workflows in hardware and robotics.

Principles

Method

Install CAD Skills via `npx skills install earthtojake/text-to-cad` or as a plugin for Codex/Claude Code. Agents then access focused workflows for CAD, fabrication, and robot description files.

In practice

Topics

Code references

Best for: Machine Learning Engineer, Computer Vision Engineer, Robotics Engineer, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.