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You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.

The automated review consistently flags patterns your team uses intentionally—force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers dismiss approximately 30% of all findings as project-specific false positives.

Which approach prevents the model from generating these findings in the first place by supplying the project’s conventions as persistent context during every review?

A.

Document the team’s accepted patterns and intentional conventions in the project’s CLAUDE.md file so the model receives this context during every review.

B.

Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates.

C.

Build post-processing keyword filters that suppress findings containing terms such as “force unwrap,” “large class,” or “deprecated import” before results reach developers.

D.

Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.

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