multi-reviewer-patterns

wshobson/agents · updated Apr 8, 2026

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$npx skills add https://github.com/wshobson/agents --skill multi-reviewer-patterns
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summary

Coordinate parallel code reviews across multiple quality dimensions with deduplication and severity calibration.

  • Allocates reviews across five dimensions (Security, Performance, Architecture, Testing, Accessibility) with recommended combinations for different code change types
  • Deduplicates findings from multiple reviewers using merge rules based on file location and issue type, with conflict resolution for severity ratings
  • Provides severity calibration criteria (Critical, High, Mediu
skill.md

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}
how to use multi-reviewer-patterns

How to use multi-reviewer-patterns on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add multi-reviewer-patterns
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill multi-reviewer-patterns

The skills CLI fetches multi-reviewer-patterns from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/multi-reviewer-patterns

Reload or restart Cursor to activate multi-reviewer-patterns. Access the skill through slash commands (e.g., /multi-reviewer-patterns) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.740 reviews
  • William Huang· Dec 8, 2024

    multi-reviewer-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Shikha Mishra· Dec 4, 2024

    Useful defaults in multi-reviewer-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Nikhil Thomas· Dec 4, 2024

    We added multi-reviewer-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Camila Anderson· Dec 4, 2024

    Keeps context tight: multi-reviewer-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Jin Sanchez· Nov 27, 2024

    Useful defaults in multi-reviewer-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Nov 23, 2024

    multi-reviewer-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Amelia Ghosh· Nov 23, 2024

    Registry listing for multi-reviewer-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ava Anderson· Nov 3, 2024

    multi-reviewer-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ava Zhang· Oct 22, 2024

    We added multi-reviewer-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Jin Park· Oct 18, 2024

    I recommend multi-reviewer-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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