Contribution Guidelines (AI‐Aware)

As AI becomes part of how code is written and reviewed, DataJourney's contribution norms evolve accordingly. This document explains why intent matters and how to contribute responsibly, with or without AI.

The Changing Era of Contributions

AI-assisted contributions are increasing rapidly. While this improves speed and accessibility, it also introduces challenges, especially when:

• Large volumes of code are generated without discussion
• Context, goals, or reasoning are unclear
• This often leads to maintainer burnout and slows meaningful collaboration.

Intent-First Contributions

Regardless of how a contribution is created (manual, AI-assisted, or collaborative), contributors should clearly communicate:

• What problem is being solved
• Think through the existing tooling in place before proposing a cutting-edge solution
• What trade-offs were considered

What we encourage

• Early discussion before large AI-generated changes
• Smaller, scoped pull requests
• Use of issues/discussions for exploration and design alignment
• Honest and clear disclosure

DataJourney: AI Contribution Disclosure

Section	What to Provide	Example
Intent and goal	What outcome were you aiming for?	Make the workflow easier for first-time contributors to run locally
Type of work	Nature of the contribution (one or more)	Workflow design, code generation, refactor
How AI was used	Role played by AI in the process	Used as a drafting assistant to generate an initial GHA workflow
Models used	Models involved, if any	GPT, open source LLMs
Prompt (optional)	Prompt or concise summary of instruction.	Generate a minimal GHA for running tests on pull requests
AI-generated output	Rough estimate of AI-generated artifacts	~120 lines generated; 40% used as-is, 50% modified
Human decisions	Key manual decisions or trade-offs	Renamed steps for clarity
Notes for reviewers	Assumptions, risks, or limitations	-----