code-change-review

name	code-change-review
description	Review AI-generated code changes for unintended consequences, logic errors, UI inconsistencies, and context-specific issues. Use when: code has just been modified, flag potential bugs, verify UI consistency, check implementation correctness against project patterns.
argument-hint	Describe the change or file(s) to review
user-invocable	true
disable-model-invocation	false

Code Change Review

When to Use

• After AI agents make modifications to code
• Before committing changes to verify quality
• When suspicious about consistency or hidden impacts
• Testing cross-cutting effects (styling, state management, routing)
• UI/UX quality check for visual consistency

Philosophy

The best changes are the ones that appear invisible — they fit seamlessly into the existing codebase without standing out. This skill helps ensure changes follow project patterns, don't introduce unnecessary complexity, and don't break subtle invariants. The goal of this is to ensure robustness and maintainability, not just to catch obvious bugs. By systematically checking for common pitfalls and patterns, we can catch issues that might be missed in a quick glance.

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Step-by-Step Procedure

1. Understand the request and scan the diff or whatever code the user wants reviewed. Identify the scope and nature of the change (local vs cross-cutting, logic vs UI). If the user does not specify, you are at that point to audit the primary source code files in the codebase, tracing them to what they power and continue your audit from there.
2. Review logic paths, side effects, and edge cases thoroughly. Check assumptions and compare against project patterns. Evaluate whether the change would hold up in production use.
3. Review UI consistency if presentation changed.
4. Check integrations, configuration impact, and documentation needs.
5. Report concrete findings with severity and suggested fixes.

Use workflow.md for the full checklists and detailed review sequence. You do not deviate from

Output

Produce a review report in the chat with:

1. Findings ranked by severity
2. Why each issue matters
3. Suggested fix or mitigation
4. Any remaining risks or testing gaps

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Common Red Flags

Watch for these patterns when reviewing AI-generated changes:

Red Flag	What It Means	Check
Overly defensive code	Too many null checks, type guards for things that shouldn't be null	Traces assumptions — are they valid?
New utility function doing work that exists elsewhere	Code duplication or missing abstraction	Search for similar logic in codebase
Magic numbers or strings	Hardcoded values that should be constants/config	Find where same values defined elsewhere
Nested ternaries or complex conditionals	Hard to reason about logic	Simplify to guard clauses or early returns
Inconsistent error handling	mix of try/catch, if-checks, silent failures	Standardize to project's error pattern
Unused variables after refactor	Leftover declarations	Search for actual usage
New dependency added without thought	Library bloat or version conflicts	Check if something similar exists in project
State mutation in pure function context	Unexpected side effects	Check whether function should be pure
CSS specificity arms race	!important, deeply nested selectors, inline styles	Migrate to design system utilities
Component too complex	Too many props, state, or nested children	Consider splitting or simplifying

For concrete examples of negative patterns to avoid and positive patterns to follow, see Pattern Reference.

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Example Usage Prompts

• /code-change-review -> "Review this change for bugs or issues I might have missed"
• /code-change-review -> "Check if this implementation fits our codebase patterns"
• /code-change-review -> "Look for UI inconsistencies in this component"

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Quick Reference

For quick reviews: Use the Common Red Flags table and spot-check 2-3 items.

For thorough reviews: Walk through workflow.md systematically.

For UI-heavy changes: Use the UI checklist in workflow.md.

For complex logic: Use the logic checklist in workflow.md and trace execution paths carefully.

JUCE 8.0.8 Code Quality Protocol (WAVFin)

When reviewing JUCE C++ audio plugin projects:

1. Memory & Threading: Check for raw pointers and ensure NO allocations or locks exist on the audio thread.
2. Modern C++ & Performance: Recommend modern C++ features where compatible, and note any performance-critical optimizations.
3. Robustness: Highlight any exception safety issues or non-real-time safe code.
4. Documentation Check: Base all recommendations on JUCE documentation via jucedoclinks.mdc.

References

• workflow.md - Full review checklists and detailed procedure
• Pattern Reference - Positive and negative implementation patterns