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📚 Aligna AI Review Guidelines for AI Agents

  • Thank you for taking the time to contribute or review — your insights are truly appreciated!
  • These guidelines aim to make reviews smooth, enjoyable, and focused on high-quality contributions.
  • Follow these guidelines as they apply to your review context.

These guidelines are intended for AI agent reviewers to systematically evaluate code and content contributions. Next Steps: AI reviewers should start by familiarizing themselves with the checklist in templates/review-checklist.md and reviewing the metrics outlined in METRICS.md to understand evaluation criteria.

⭐ Principles We Aim For

  • Clarity: Code and documentation should be clear and easy to follow.
  • Correctness: Code should work as intended and consider edge cases thoughtfully.
  • Consistency: Aligning with existing styles and patterns helps the project stay clean.
  • Minimalism: Prefer simpler solutions that are easier to maintain.
  • Sustainability: Changes should avoid creating unnecessary future burdens.

✅ Helpful Things to Check During Review

  • Is the purpose of the change clear and understandable?
  • Is the code and documentation easy to read at a glance?
  • Are edge cases and failure modes thoughtfully considered?
  • Are related documentation, examples, or tests updated if relevant?
  • (Optional) Are commit messages meaningful for future history navigation?

⚠️ Common Pitfalls to Watch For

  • Adding unnecessary complexity without clear benefits.
  • Forgetting about important edge cases or the user experience.
  • Submitting exceptionally large pull requests without clear logical separations.
  • (Optional) Using vague commit messages that could confuse later.

❓ Important Philosophy: No Assumptions

  • If something feels unclear, ask or clarify rather than assuming.
  • Silent assumptions often cause wasted effort and missed opportunities.
  • Asking early saves everyone's time and strengthens the project.

Also see the Repository README for more context.