Perfect your pull request and disclose you’ve used AI [AI Skills Series - Week Two] #168984
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Here's my submission for Scenario 5: Refactoring code. Commit Message:
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Scenario 3 Commit Message |
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One more challenge to take you to the next level as a responsible AI contributor, come play code critic in our final challenge discussion. |
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/assign |
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Scenario 1: Add unit testsCommit Message
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Welcome to part two of our AI Skills Series. This week we’ll be writing a pull request commit message that discloses how you’ve used AI. As a reminder, those who participate will be entered to win one of 10 GitHub Shop vouchers.
Challenge
Pick one of the scenarios in the comments and write a commit message for it!
Scenario one: You’re writing a new unit test and are unsure if you’ve covered all edge cases. You ask GitHub Copilot for advice on potential edge cases to test. You select one suggestion and write the test logic yourself.
Scenario two: A function in the codebase is slow and needs optimization. Maintenance has requested more efficient code. You use GitHub Copilot to generate an optimized version of the function based on your description. You then review, adapt variable names, add comments, and thoroughly test the output.
Scenario three: The project is adding French language support. There are dozens of new UI strings that need translation. You use AI to translate all new strings, then work with a fluent French speaker in the community to review and correct the translations before submitting.
Scenario four: The maintainers invite proposals for a new plugin system. You want your proposal to be thorough and well-organized. You use AI to brainstorm ideas, outline the proposal, and draft much of the initial text. You then refine the proposal, add project-specific context, and verify technical feasibility before submission.
Scenario five: A utility function is slow and hard to read. The maintainers suggest refactoring for efficiency and clarity. You describe the function’s purpose to an AI code generator, which outputs a new, optimized implementation. You run tests, review for correctness, and add inline comments to clarify logic.
Examples
Good AI Disclosure Examples:
Co-developed with AI - Human architecture decisions, AI implementation assistance for socket management
Bad AI Disclosure Examples:
Resources and tools
*No Purchase Necessary. Open only to Github community members 18+. Game ends 8/21/25. For details, see Official Rules.
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