Multi Reviewer Patterns

Multi-Reviewer Patterns

Patterns for coordinating parallel code reviews across multiple quality dimensions, deduplicating findings, calibrating severity, and producing consolidated reports.

When to Use This Skill

• Organizing a multi-dimensional code review
• Deciding which review dimensions to assign
• Deduplicating findings from multiple reviewers
• Calibrating severity ratings consistently
• Producing a consolidated review report

Review Dimension Allocation

Available Dimensions

Dimension	Focus	When to Include
Security	Vulnerabilities, auth, input validation	Always for code handling user input or auth
Performance	Query efficiency, memory, caching	When changing data access or hot paths
Architecture	SOLID, coupling, patterns	For structural changes or new modules
Testing	Coverage, quality, edge cases	When adding new functionality
Accessibility	WCAG, ARIA, keyboard nav	For UI/frontend changes

Recommended Combinations

Scenario	Dimensions
API endpoint changes	Security, Performance, Architecture
Frontend component	Architecture, Testing, Accessibility
Database migration	Performance, Architecture
Authentication changes	Security, Testing
Full feature review	Security, Performance, Architecture, Testing

Finding Deduplication

When multiple reviewers report issues at the same location:

Merge Rules

1. Same file:line, same issue — Merge into one finding, credit all reviewers
2. Same file:line, different issues — Keep as separate findings
3. Same issue, different locations — Keep separate but cross-reference
4. Conflicting severity — Use the higher severity rating
5. Conflicting recommendations — Include both with reviewer attribution

Deduplication Process

For each finding in all reviewer reports:
  1. Check if another finding references the same file:line
  2. If yes, check if they describe the same issue
  3. If same issue: merge, keeping the more detailed description
  4. If different issue: keep both, tag as "co-located"
  5. Use highest severity among merged findings

Severity Calibration

Severity Criteria

Severity	Impact	Likelihood	Examples
Critical	Data loss, security breach, complete failure	Certain or very likely	SQL injection, auth bypass, data corruption
High	Significant functionality impact, degradation	Likely	Memory leak, missing validation, broken flow
Medium	Partial impact, workaround exists	Possible	N+1 query, missing edge case, unclear error
Low	Minimal impact, cosmetic	Unlikely	Style issue, minor optimization, naming

Calibration Rules

• Security vulnerabilities exploitable by external users: always Critical or High
• Performance issues in hot paths: at least Medium
• Missing tests for critical paths: at least Medium
• Accessibility violations for core functionality: at least Medium
• Code style issues with no functional impact: Low

Consolidated Report Template

## Code Review Report

**Target**: {files/PR/directory}
**Reviewers**: {dimension-1}, {dimension-2}, {dimension-3}
**Date**: {date}
**Files Reviewed**: {count}

### Critical Findings ({count})

#### [CR-001] {Title}

**Location**: `{file}:{line}`
**Dimension**: {Security/Performance/etc.}
**Description**: {what was found}
**Impact**: {what could happen}
**Fix**: {recommended remediation}

### High Findings ({count})

...

### Medium Findings ({count})

...

### Low Findings ({count})

...

### Summary

| Dimension    | Critical | High  | Medium | Low   | Total  |
| ------------ | -------- | ----- | ------ | ----- | ------ |
| Security     | 1        | 2     | 3      | 0     | 6      |
| Performance  | 0        | 1     | 4      | 2     | 7      |
| Architecture | 0        | 0     | 2      | 3     | 5      |
| **Total**    | **1**    | **3** | **9**  | **5** | **18** |

### Recommendation

{Overall assessment and prioritized action items}