| name | review-ai-code-quality |
| description | Review AI-generated or recently changed code for common quality failures. Use when reviewing code, validating an implementation, checking "is this ready", auditing AI-authored changes, or looking for edge cases, validation gaps, error handling problems, duplication, and needless complexity. Produces prioritized findings and required fixes. |
Review AI Code Quality
AI-authored code often looks plausible while missing boring safeguards. Review the boring safeguards first.
1. Identify the changed surface
Inspect the diff or target files. Determine:
- Inputs and trust boundaries
- External calls
- State mutations
- Error paths
- User-visible behavior
- Tests or checks that cover the change
2. Run the Big Five review
Input validation
Every external input should be parsed or validated at the boundary. Look for raw request bodies, unchecked params, untyped JSON, unsafe casts, and string-built queries.
Edge cases
Check null, empty arrays, empty strings, zero values, unicode, timeouts, large inputs, duplicate inputs, and missing optional fields.
Error handling
No empty catches. No swallowed errors on important paths. User-facing messages should be safe and logs should include context.
Duplication
Repeated logic in three or more places should usually become one helper. Two copies may be fine; do not abstract too early.
Complexity
Flag deeply nested conditionals, large functions, multi-purpose modules, and clever code with unclear invariants.
3. Check tests
Ask:
- Is the main success path covered?
- Are failure paths covered?
- Are edge cases covered?
- Do assertions check meaningful behavior?
- Would the test fail if the implementation were wrong?
High coverage with weak assertions is not quality.
