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To automate code review fixes, the Skill triages pull request findings into high-confidence categories for auto-application and lower-confidence categories for interactive review, creating and pushing commits after resolving issues.

The best way to apply code review findings sequentially is to use an automated triage system that processes high-confidence issues automatically and guides you through lower-confidence fixes interactively before committing.

Yes, you can automatically resolve obvious code review suggestions by categorizing findings by confidence level, where high-confidence items are auto-applied and complex ones are presented for interactive resolution.

Yes, automated code review fixing handles commit creation and pushing by applying quality gates after sequentially or interactively resolving review findings from your pull request comments.

Triaging code review findings by confidence level works by categorizing issues into high-confidence groups for automatic application and lower-confidence groups for one-by-one interactive review, ensuring safe automated fixes.

You should use interactive review over automatic application for code fixes when dealing with lower-confidence or complex findings that require clear options and manual selection to ensure correct refactoring and debugging outcomes.