What happens if a reviewer suggests a change that breaks the build?

The skill instructs the AI to perform a technical verification first. If a suggestion is found to be breaking, the AI will push back with specific technical reasoning and data.

Does this skill work with external GitHub reviewers?

Yes, it includes specific handling for external feedback, prompting the AI to be skeptical, check for regressions, and ensure the reviewer understands the full context before making changes.

How does this skill handle polite or performative AI behavior?

It explicitly forbids performative agreement and gratitude expressions like 'Great point!' or 'Thanks!', replacing them with technical acknowledgments and direct code actions.

How does it manage large lists of feedback items?

It follows a strict implementation order: clarify all unclear items first, then implement blocking issues, simple fixes, and complex refactors sequentially with individual testing for each.

Code Review Reception

Name: Code Review Reception
Author: Cycleaddict

byCycleaddict

0•

Collaboration Tools

Processes code review feedback with technical rigor and verification, prioritizing codebase reality over performative agreement.

The Code Review Reception skill provides a structured framework for Claude to handle feedback from human partners and external reviewers with skepticism and technical accuracy. Instead of blind implementation or social pleasantries, this skill enforces a 'verify-first' approach that requires restating requirements, checking suggestions against the codebase, and pushing back on technically unsound or unnecessary (YAGNI) changes. By eliminating performative gratitude and focusing on technical evaluation, it ensures that every modification is intentional, tested, and aligned with existing architectural decisions.

Key Features

01Anti-performative filters to remove empty social agreement and gratitude

020 GitHub stars

03Technical pushback mechanisms for incorrect or conflicting suggestions

04YAGNI verification to prevent over-engineering suggested by reviewers

05Standardized six-step response pattern for processing feedback

06Structured multi-item handling to ensure all feedback is clarified before work begins

Use Cases

01Evaluating external suggestions against legacy constraints and backward compatibility requirements

02Processing complex pull request feedback from multiple reviewers while maintaining architectural integrity

03Clarifying ambiguous instructions from human leads to avoid partial or incorrect implementations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add cycleaddict/generic-superpowers receiving-code-review

For use in Claude.ai and ChatGPT

Download Skill