Mmiv Ml Fastmonai Code Reviewer

Code Reviewer

Comprehensive code review analysis covering quality, security, and performance with Python/fastMONAI specialization.

When to Use

Proactively activate this skill when:

• Reviewing code changes before commit
• Analyzing new implementations or features
• Detecting potential issues in modified files
• Asked to assess code quality or find problems
• Completing significant code writing tasks (self-review)

Review Process

1. Understand Context First

Before reviewing, establish:

• Purpose of the code (feature, bugfix, refactor)
• Integration points with existing codebase
• Criticality level (production, research, prototype)

2. Apply Review Layers

Review code through three lenses, in order of priority:

Layer 1: Code Quality

• Readability: Clear naming, appropriate comments, logical organization
• Maintainability: Single responsibility, DRY principles, appropriate abstraction level
• Correctness: Logic errors, edge cases, error handling
• Style: PEP 8 compliance, consistent formatting, idiomatic Python

Layer 2: Security

• Input validation: Untrusted data handling, injection risks
• Data exposure: Sensitive data logging, hardcoded secrets
• Resource safety: File operations, network calls, subprocess usage
• Dependencies: Known vulnerabilities, version pinning

Layer 3: Performance

• Algorithmic efficiency: Time/space complexity, unnecessary iterations
• Memory management: Large object handling, memory leaks in loops
• I/O optimization: Batch operations, caching opportunities
• Medical imaging specific: Tensor operations, GPU memory, large volume handling

3. fastMONAI/nbdev Specific Checks

When reviewing fastMONAI code:

• Verify changes are in .ipynb notebooks, not .py files directly
• Check #| export directives are correct
• Ensure #| notest is used appropriately for expensive operations
• Validate padding_mode=0 usage with TorchIO CropOrPad
• Confirm preprocessing consistency between training and inference
• Check MedImage/MedMask type usage for medical data

Reference references/python-patterns.md for detailed Python anti-patterns and best practices.

Output Format Guidelines

Adapt output based on context:

For Small Changes (< 50 lines)

Use inline format with line references:

[file.py:42](file.py#L42) - Issue: Variable `x` shadows outer scope
[file.py:55-60](file.py#L55-L60) - Suggestion: Extract to helper function

For Large Changes or Full Reviews

Use structured report:

## Review Summary
Brief overview of findings

## Critical Issues
Must-fix problems (security, correctness)

## Improvements
Recommended changes (quality, performance)

## Positive Observations
Well-implemented patterns worth noting

## Questions
Clarifications needed from author

For Self-Review (after writing code)

Brief checklist verification:

• Tested edge cases
• Error handling complete
• No obvious security issues
• Performance acceptable

Severity Classification

• Critical: Security vulnerabilities, data loss risks, crash bugs
• High: Logic errors, significant performance issues, maintainability blockers
• Medium: Code smell, minor inefficiencies, style inconsistencies
• Low: Nitpicks, optional improvements, documentation gaps

Review Tone

• Be constructive, not critical
• Explain the "why" behind suggestions
• Acknowledge good patterns alongside issues
• Provide concrete fix suggestions when possible
• Ask questions rather than assume intent

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