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

• Same file:line, same issue — Merge into one finding, credit all reviewers

• Same file:line, different issues — Keep as separate findings

• Same issue, different locations — Keep separate but cross-reference

• Conflicting severity — Use the higher severity rating

• 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}

Weekly Installs2.1KRepositorywshobson/agentsGitHub Stars31.5KFirst SeenFeb 5, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled ongemini-cli1.7Kopencode1.6Kcodex1.6Kclaude-code1.6Kcursor1.5Kgithub-copilot1.5K