How does the skill handle conflicts between different reviewers?

If external feedback (like from a linter or third party) conflicts with a human partner's instructions, the skill requires a discussion with the human partner before proceeding.

Why does this skill forbid 'thank you' and other social responses?

The skill prioritizes technical correctness and efficiency. By removing performative agreement, it ensures the interaction remains focused on codebase reality and technical verification.

How does arc-receiving-review handle unclear feedback?

If any feedback item is ambiguous, the skill mandates an immediate stop. The agent must ask for clarification on all items before attempting any implementation to prevent incorrect changes.

What is the benefit of the YAGNI check during a review?

Reviewers often suggest 'proper' implementations for features that aren't actually used. This skill greps the codebase to verify usage, preventing the addition of unnecessary code.

Technical Feedback Rigor

Name: Technical Feedback Rigor
Author: GregoryHo

byGregoryHo

0•

Collaboration Tools

Standardizes the processing of code review feedback by prioritizing technical verification and codebase reality over performative agreement.

The arc-receiving-review skill equips AI agents with a disciplined framework for processing code review feedback. Instead of defaulting to passive agreement or social pleasantries, this skill enforces a rigorous 'Verify before implementing' mindset. It guides Claude through a structured response pattern—Read, Understand, Verify, Evaluate, Respond, and Implement—ensuring that every change is technically sound for the specific codebase. By eliminating performative gratitude and mandating YAGNI (You Aren't Gonna Need It) checks, it maintains high architectural standards and prevents the introduction of unnecessary complexity or bloat during the review cycle.

Key Features

01Elimination of performative and social filler responses

02Strict protocols for resolving conflicting human and external feedback

03Automated YAGNI checks against actual codebase usage

04Mandatory stop-and-clarify triggers for ambiguous requirements

050 GitHub stars

06Structured 'Verify before implementing' feedback loop

Use Cases

01Reconciling feedback between automated linting tools and human project leads

02Processing PR comments with technical precision and architectural awareness

03Evaluating suggested 'best practices' against the actual needs of the current stack

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gregoryho/arcforge arc-receiving-review

For use in Claude.ai and ChatGPT

Download Skill