How does this skill handle conflicting reviewer feedback?

It applies specific merge rules to consolidate findings, prioritizing the highest severity rating and including both recommendations with reviewer attribution when suggestions conflict.

How does severity calibration work?

It uses a rubric based on impact and likelihood to categorize findings into Critical, High, Medium, or Low buckets, ensuring consistency across different reviewers.

What dimensions are covered in the review patterns?

The skill includes patterns for Security, Performance, Architecture, Testing, and Accessibility, with recommended combinations based on the type of code change.

When should I use the multi-reviewer pattern?

It is best used for complex PRs, such as database migrations, authentication changes, or full feature reviews that require scrutiny from multiple technical perspectives.

Can I use this for automated report generation?

Yes, the skill includes a standardized Markdown template for producing consolidated reports that summarize findings by severity and dimension.

Multi-Reviewer Code Review Orchestrator

Name: Multi-Reviewer Code Review Orchestrator
Author: gwickman

bygwickman

0•

协作工具

Coordinates parallel, multi-dimensional code reviews with automated finding deduplication and severity calibration.

This skill provides a structured framework for managing complex code reviews by coordinating multiple reviewers across specialized dimensions such as security, performance, architecture, and accessibility. It helps teams organize review assignments, resolve overlapping feedback through intelligent merge rules, and standardize severity ratings to ensure consistency across the codebase. By generating consolidated reports from disparate findings, it reduces the administrative overhead of peer reviews while improving the quality and clarity of technical feedback during the development lifecycle.

主要功能

01Standardized severity calibration based on impact and likelihood

02Finding deduplication with automated merge rules for co-located issues

030 GitHub stars

04Consolidated report generation with prioritized action items

05Multi-dimensional review allocation for security, performance, and UI

06Conflict resolution patterns for differing reviewer recommendations

使用场景

01Standardizing severity levels and reporting formats for cross-functional team PRs

02Coordinating specialized security and performance audits for high-traffic API changes

03Consolidating feedback from multiple stakeholders during major architectural refactors

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gwickman/auto-dev-test-target-1 multi-reviewer-patterns

For use in Claude.ai and ChatGPT

Download Skill