pal-codereview

majiayu000's avatarfrom majiayu000

Systematic code review covering quality, security, performance, and architecture using PAL MCP. Use for pull request reviews, code audits, or pre-commit validation. Triggers on review requests, PR reviews, or code quality checks.

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[Code Review]

When & Why to Use This Skill

The PAL Code Review skill is a comprehensive AI-driven diagnostic tool designed to perform systematic evaluations of source code. By leveraging the PAL MCP, it provides expert-level validation across four critical dimensions: code quality, security vulnerabilities, performance optimization, and architectural integrity. It serves as an automated peer reviewer that helps engineering teams maintain high standards, reduce technical debt, and identify critical risks before deployment.

Use Cases

  • Automated Pull Request Reviews: Streamline the PR process by automatically scanning changes for logic errors, style inconsistencies, and best practice violations.
  • Security-Focused Audits: Conduct deep-dive inspections for vulnerabilities such as SQL injection, authentication bypasses, and improper data exposure in sensitive modules.
  • Performance Bottleneck Identification: Analyze code for inefficient database queries, memory leaks, or algorithmic complexities that could impact system scalability.
  • Pre-merge Architectural Validation: Ensure new code contributions align with established architectural patterns and separation of concerns before they enter the main branch.
  • Compliance and Standards Enforcement: Use custom focus areas and severity filters to ensure all code meets specific organizational security policies or industry regulations.
namepal-codereview
descriptionSystematic code review covering quality, security, performance, and architecture using PAL MCP. Use for pull request reviews, code audits, or pre-commit validation. Triggers on review requests, PR reviews, or code quality checks.

PAL Code Review

Structured code review with expert validation across quality, security, performance, and architecture.

When to Use

  • Pull request reviews
  • Pre-merge code audits
  • Security-focused reviews
  • Performance reviews
  • Code quality assessments

Quick Start

# Start a code review
result = mcp__pal__codereview(
    step="Reviewing authentication changes for security and quality",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Beginning comprehensive review",
    review_type="full",
    relevant_files=[
        "/app/auth/login.py",
        "/app/auth/tokens.py"
    ],
    confidence="exploring"
)

Review Types

Type Focus
full Comprehensive: quality, security, performance, architecture
security Security vulnerabilities and auth issues
performance Bottlenecks and optimization
quick Fast pass for obvious issues

Validation Types

Type Description
external Uses expert model for validation (default)
internal Local-only review, no external validation

Required Parameters

Parameter Type Description
step string Review narrative
step_number int Current step
total_steps int Total steps (usually 2 for external)
next_step_required bool More review needed?
findings string Review findings

Optional Parameters

Parameter Type Description
review_type enum full/security/performance/quick
review_validation_type enum external/internal
severity_filter enum critical/high/medium/low/all
focus_on string Specific emphasis (e.g., "threading")
standards string Coding standards to enforce
relevant_files list Files under review
issues_found list Issues with severity
continuation_id string Continue session

Issue Severity Levels

issues_found=[
    {"severity": "critical", "description": "SQL injection vulnerability"},
    {"severity": "high", "description": "Missing authentication check"},
    {"severity": "medium", "description": "Inefficient database query"},
    {"severity": "low", "description": "Inconsistent naming convention"}
]

Example: Security Review

mcp__pal__codereview(
    step="Security-focused review of payment processing code",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Scanning for injection, auth bypass, data exposure",
    review_type="security",
    severity_filter="medium",  # Report medium and above
    relevant_files=[
        "/app/payments/processor.py",
        "/app/payments/validation.py"
    ],
    focus_on="input validation, PCI compliance",
    confidence="exploring"
)

Review Checklist

Quality

  • Code readability and clarity
  • Appropriate abstractions
  • Error handling
  • Test coverage

Security

  • Input validation
  • Authentication/authorization
  • Secrets management
  • Injection prevention

Performance

  • Database query efficiency
  • Caching strategy
  • Algorithm complexity
  • Resource cleanup

Architecture

  • Separation of concerns
  • Dependency management
  • API design
  • Scalability considerations

Best Practices

  1. Cover all aspects - Quality, security, performance, architecture
  2. Prioritize findings - Critical issues first
  3. Be constructive - Suggest improvements, not just problems
  4. Reference code - Use file paths and line numbers
  5. Consider context - Time pressure, team experience