coderabbit-cli_skill

Overview

This skill uses the CodeRabbit CLI to perform automated, structured code reviews and drive iterative improvement inside AI agent workflows. It helps agents catch correctness, security, readability, and maintainability issues before human review or submission. Requires the CodeRabbit CLI to be installed and authenticated and a git repository with local changes to review.

How this skill works

Run the CodeRabbit CLI from your repository to analyze unstaged, staged, or local commits and receive categorized feedback (correctness, readability, maintainability, security, and performance). Use detailed mode (--plain) for full recommendations or prompt-only (--prompt-only) for a token-efficient summary. Apply fixes, document rationale for decisions, and re-run reviews to close the loop until quality goals are met.

When to use it

• After implementing non-trivial code: new features, refactors, or algorithms
• Before submitting a pull request to catch major and security issues early
• When improving code quality, readability, or maintainability
• Exploring unfamiliar languages, frameworks, or domain patterns
• Creating an automated self-reviewing coding loop in an AI agent workflow

Best practices

• Install and authenticate the CLI (coderabbit auth login) and run from a git repo with local changes
• Use --plain during active development for comprehensive feedback and --prompt-only for token-constrained runs
• Prioritize correctness and security fixes; apply stylistic suggestions selectively based on project conventions
• Stage only the changes you want reviewed or use --type/--base to limit scope and avoid file-count limits
• Document rationale whenever you intentionally ignore or defer suggestions, and re-run reviews after significant fixes

Example use cases

• Agent implements a new feature in TypeScript and runs coderabbit review --plain to find logic and typing issues
• Before opening a PR, stage changes and run coderabbit review to surface security and maintainability concerns
• Refactoring legacy code: run reviews to ensure no regressions and validate suggested improvements
• Learning a new framework: implement an initial solution and use prompt-only review to get concise idiomatic guidance
• Automated agent loop: implement → review → apply fixes → re-review until the quality bar is met