multi-reviewer-patterns
Enables your agent to coordinate parallel code reviews, deduplicating findings and consolidating reports.
What it does
This tool helps organize complex code reviews, ensuring different aspects (like security and performance) are checked by multiple people. It also helps consolidate similar issue reports and consistently rate the severity of problems.
When to use it
- ✓When organizing a code review with multiple reviewers.
- ✓When deciding which code aspects (security, performance, etc.) should be reviewed.
- ✓When consolidating issue reports from different reviewers to avoid duplicates.
Example of what to ask your AI
“I need to organize a code review for a new login feature. What aspects should I prioritize and how should I rate the issues found?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool defines different review focus areas (dimensions) and suggests combinations for different types of changes. It also sets rules for grouping identical issues and for defining the severity of each problem found.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill multi-reviewer-patternsOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/agent-teams/skills/multi-reviewer-patterns/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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 |
…
FAQ
What are review 'dimensions'?
These are the different aspects that can be checked in the code, such as security, performance, architecture, testing, and accessibility.
How does the tool handle duplicate issues?
If multiple reviewers find the same issue in the same location, it's merged into a single item. If the issues are different but in the same location, they are listed separately. If the severity differs, the higher one is used.
How is issue severity determined?
Severity (Critical, High, Medium, Low) is defined based on potential impact (what could happen) and likelihood of occurrence. For example, a security breach is Critical, while a style issue is Low.
Details
Original description by the author
Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4794
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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