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name: code-review description: Automated code review for pull requests using multiple specialized agents with confidence-based scoring to filter false positives license: MIT compatibility: opencode

Code Review Skill

Automated code review patterns for pull requests with confidence-based scoring to minimize false positives.

What I Do

I review pull requests by examining:

• Code Quality - Readability, maintainability, consistency
• Potential Bugs - Edge cases, null checks, error handling
• Performance - Inefficient algorithms, unnecessary computations
• Type Safety - Missing types, incorrect type usage
• Best Practices - Language and framework conventions
• Testing Coverage - Missing test cases, untested code paths

Review Process

1. Understand Context - What changes were made and why?
2. Check Patterns - Are established patterns being followed?
3. Identify Issues - List problems with severity levels
4. Suggest Improvements - Provide actionable recommendations
5. Prioritize - Flag critical vs. nice-to-have issues

Output Format

Group findings by severity:

• Critical - Bugs, security issues, major design flaws
• Important - Performance issues, anti-patterns
• Suggestion - Style, naming, minor improvements

Confidence-Based Filtering

Not every issue needs to be reported. Filter out:

• Stylistic preferences that don't affect functionality
• Minor optimizations with negligible impact
• Personal preferences that follow established patterns

When in Doubt

• Ask the user for their preference
• Check the project's existing patterns
• Err on the side of not reporting marginal issues

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Part of SuperAI GitHub - Centralized OpenCode Configuration