GitHub Introduces Custom Instructions for Copilot Code Review

GitHub Enhances Copilot with Custom Instructions

GitHub has announced a significant update to its Copilot code review feature, now allowing users to customize their code review processes with personalized instructions. This enhancement aligns with the existing customization options available in Copilot Chat and the Copilot coding agent, providing a consistent experience across an organization's workflow, according to GitHub Blog.

Customizable Code Reviews for Enhanced Workflow

The new feature utilizes a customization file, .github/copilot-instructions.md, enabling users to define natural language instructions tailored to their specific needs. This approach allows developers to instruct Copilot on various preferences, such as language, code readability, and focus areas. Examples include instructing Copilot to "respond in Spanish" for multilingual teams or to "focus on readability and avoid nested ternaries" for style-conscious reviews.

Accessibility and Implementation

Currently in public preview, the customization feature is accessible to all paid Copilot users. Once the copilot-instructions.md file is added to a repository, the instructions are automatically applied, eliminating the need for manual setup for each pull request. Organizations can opt into this feature through their administrators, ensuring that all team members benefit from the enhanced functionality.

Future Developments and Community Engagement

GitHub plans to roll out additional management options for this feature, which will be accessible via the repository settings under Copilot code review. This upcoming development aims to provide users with more control over the behavior of Copilot during code reviews. In the meantime, GitHub encourages users to participate in discussions within the GitHub Community, fostering an environment of shared insights and collaborative learning.

Conclusion

With these new capabilities, GitHub continues to enhance its AI-driven tools, helping developers streamline their workflows and ensure more consistent and personalized code reviews. As the technology evolves, users can expect further improvements and features that cater to diverse coding needs.