dxf

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Generate, regenerate, and validate 2D DXF drawings from Python build123d sources. Use for DXF files, `.py` drawing scripts, @dxf models, 2D profiles, outlines, templates, gaskets, panels, flat patterns, laser/plasma/waterjet cut layouts, and 2D drawing exports of CAD geometry.

AI & Automation 957 stars 79 forks Updated today MIT

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Skill Content

# DXF generation and validation Provenance: maintained in [earthtojake/text-to-cad](https://github.com/earthtojake/text-to-cad). Use the installed local skill files as the runtime source of truth; the repository link is only for provenance and release review. ## Setup This skill's commands are thin entrypoints over the `cadgen` distribution, which carries the Python build runtime and the JavaScript it executes. Install it once: ```bash python -m pip install -r requirements.txt ``` Drawings are build123d geometry, so a drawing build loads the CAD kernel like a STEP build does (~2.5s cold; the warm daemon absorbs it on re-runs). Only `cadgen dxf snapshot` additionally needs **Node 20 or newer on `PATH`** — it meshes the flat pattern on demand through a bundled Node one-shot; a missing `node` is reported at render time. ## Purpose Create or modify 2D DXF drawings from natural-language requirements or from CAD geometry, generate validated drawing artifacts, and return checked outputs. A DXF drawing's source of truth is a Python file named `<name>.py` defining one parameterless `@dxf` model function. **A drawing is a model.** It has the same wrapper, record, freshness gate and build job a `@step` part has; its one output is the `.dxf` file; it has no geometry tree (nothing links to a drawing). Every run writes the sibling `<name>.dxf` (or the `out=` the decorator names); an unchanged source is a no-op; a drawing that calls a part model — `bracket()` inside its body — is st...

Details

Author
autonomous-ai
Repository
autonomous-ai/openharness
Created
1 months ago
Last Updated
today
Language
C
License
MIT

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