mirror of
https://github.com/manyfold3d/manyfold.git
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37 lines
2.3 KiB
Markdown
37 lines
2.3 KiB
Markdown
# Contributing
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We love pull requests from everyone. By participating in this project, you
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agree to abide by our [code of conduct](https://github.com/manyfold3d/manyfold?tab=coc-ov-file#coc-ov-file).
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Fork, then clone the repo:
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git clone git@github.com:your-username/manyfold.git
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Get the app running locally by following these [instructions](https://github.com/manyfold3d/manyfold?tab=readme-ov-file#running-locally):
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Then make sure the tests pass:
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rake
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Make your change. Add tests for your change. Make sure the old and new tests both pass!
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Push to your fork and [submit a pull request](https://github.com/manyfold3d/manyfold/compare/).
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We try to at least comment on pull requests within a few days (and normally much
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quicker). We may suggest some changes, improvements, or alternatives.
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Some things that will increase the chance that your pull request is accepted:
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* Sign our simple [Contributor License Agreement](https://cla-assistant.io/manyfold3d/manyfold) agreeing to license your code as MIT
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* Write tests.
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* Check that your code passes our [linter](https://github.com/manyfold3d/manyfold?tab=readme-ov-file#coding-standards).
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* Write a [good commit message](https://www.freecodecamp.org/news/how-to-write-better-git-commit-messages/).
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## Generative AI policy
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We are highly unlikely to review or accept AI-generated or "vibe coded" PRs; our time is very limited, and we need to know that *you* understand exactly what the code is doing and why, so that we can review it effectively with you.
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In addition, AI-generated code is likely incompatible with our license and CLA, as the author cannot guarantee that it does not reproduce copyrighted or FOSS-incompatible material. See the [NLNet generative AI policy](https://nlnet.nl/foundation/policies/generativeAI/) for more explanation of this and how it could even jeopardise our funding.
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If you really *must* submit AI-generated code, you will need to include prompt provenance including the model used, dates and full content of prompts, and the unedited LLM output. Again, see [the NLNet generative AI policy](https://nlnet.nl/foundation/policies/generativeAI/#transparency), and the article [Track Prompt Provenance in Version Control](https://medium.com/@psluaces/track-prompt-provenance-in-version-control-8746e2bad3c2) by Pablo Santos Luaces.
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