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LGJ-Jonathan
LGJ-JonathanOFFICIAL
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receiving-code-review

Guides verification and implementation of code review feedback with technical rigor.

Updated Mar 26, 2026
Search Tags:#code review#feedback#pull request#github#verification#yagni#pushback
One-click install
npx skills add https://github.com/LGJ-Jonathan/Jonathan_Global_Claude_Skills --skill receiving-code-review-lgj-jonathan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: receiving-code-review
Source: https://github.com/LGJ-Jonathan/Jonathan_Global_Claude_Skills/tree/main/superpowers/skills/receiving-code-review
Command: npx skills add https://github.com/LGJ-Jonathan/Jonathan_Global_Claude_Skills --skill receiving-code-review-lgj-jonathan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? When receiving code review feedback, AI assistants often respond with performative agreement or blindly implement suggestions without verifying them against the actual codebase. This Skill enforces a disciplined response pattern: read, understand, verify, evaluate, then implement or push back with technical reasoning. ## Core Features & Use Cases - Structured Response Pattern: A six-step workflow (read, understand, verify, evaluate, respond, implement) that replaces reflexive agreement with technical evaluation. - Source-Specific Handling: Different rules for feedback from a trusted human partner versus external reviewers, including YAGNI checks for unused features and pushback guidance when suggestions break existing functionality. - Use Case: A reviewer suggests removing legacy code. Instead of agreeing, you check the build target, discover the API requires a newer OS version, and respond with the technical constraint plus a concrete question about dropping backward compatibility. ## Quick Start Use the receiving-code-review skill to evaluate this reviewer's feedback against my codebase before implementing anything.

Frequently Asked Questions about receiving-code-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How should I respond to code review feedback as an AI assistant?▼

Follow a six-step pattern: read the full feedback, restate the requirement, verify against the codebase, evaluate technical soundness, respond with acknowledgment or reasoned pushback, then implement one item at a time with testing. Avoid performative phrases like "You're absolutely right!"

How do I handle unclear code review comments?▼

Stop and ask for clarification before implementing anything. Partial understanding leads to wrong implementations, especially when items are related. State which items you understand and which need clarification before proceeding.

When should I push back on a code review suggestion?▼

Push back when a suggestion breaks existing functionality, violates YAGNI for unused features, is technically incorrect for your stack, or conflicts with prior architectural decisions. Use technical reasoning and reference working code rather than defensiveness.

Should I treat external reviewer feedback differently from my team lead's?▼

Yes. Feedback from your trusted human partner can be implemented after understanding, while external reviewer suggestions require verification against the codebase first. If external feedback conflicts with prior decisions, discuss with your human partner before acting.

How do I reply to inline GitHub review comments?▼

Reply within the comment thread using the GitHub API endpoint for pull request comment replies, not as a top-level PR comment. This keeps the discussion attached to the specific line of code being reviewed.

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