multi-reviewer-patterns by wshobson

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}

Third-party checks

No alerts16d5 checks · Risk SAFE

Gen Agent Trust Hub16d
This skill provides a structured framework for conducting multi-reviewer code reviews, including deduplication logic, severity criteria, and checklists for security, performance, and accessibility. It contains no executable code or external dependencies and adheres to security best practices.
Socket16d
No alerts
Snyk16d
Risk: LOW · No issues
Runlayer7mo
2 files scanned · No issues
ZeroLeaks5mo
Score: 93/100 · 2 sections analyzed

What it does

Coordinates parallel code reviews across multiple quality dimensions (security, performance, architecture, testing, accessibility) with rules for deduplicating findings, calibrating severity consistently, and consolidating results into a single report. Use this skill when running multi-reviewer audits or need to merge overlapping findings with standardized severity ratings.

Generated from the current SKILL.md.

Frequently asked

How do I decide which review dimensions to assign to different reviewers?

The skill provides a table of five dimensions (Security, Performance, Architecture, Testing, Accessibility) with guidance on when to include each. For example, always include Security for code handling user input, include Performance for data access changes, and include Accessibility for UI changes. Recommended combinations are provided for common scenarios like API endpoints, frontend components, and authentication changes.

What do I do when multiple reviewers report the same issue at the same location?

Merge the findings into one, crediting all reviewers. Use the higher severity rating if reviewers disagree, and keep the more detailed description. If reviewers report different issues at the same location, keep both as separate findings and tag them as co-located.

How should I calibrate severity ratings across different reviewers?

The skill provides explicit severity criteria (Critical, High, Medium, Low) based on impact and likelihood. Use the calibration rules provided: security vulnerabilities exploitable by external users are always Critical or High, performance issues in hot paths are at least Medium, and code style issues with no functional impact are Low.

What format should the consolidated review report follow?

The skill includes a template that organizes findings by severity level (Critical, High, Medium, Low), includes location and dimension for each finding, and concludes with a summary table showing finding counts by dimension and severity, plus an overall recommendation.

Generated from the current SKILL.md. These answers refresh after source changes.

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SKILL.md