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javierhbr
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receive-feedback

Verify code review feedback against the codebase before implementing changes.

Updated Feb 17, 2026
Search Tags:#code quality#code review#pull request#verification#llm#feedback processing
One-click install
npx skills add https://github.com/javierhbr/random-poc --skill receive-feedback-javierhbr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: receive-feedback
Source: https://github.com/javierhbr/random-poc/tree/main/custom-skills/beagle-main/plugins/beagle-core/skills/receive-feedback
Command: npx skills add https://github.com/javierhbr/random-poc --skill receive-feedback-javierhbr

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured and rigorous process for evaluating and acting upon code review feedback, ensuring technical correctness and efficiency over performative agreement.

Core Features & Use Cases

  • Verification First: Automatically verifies feedback claims against the codebase before implementation.
  • Decision Matrix: Applies a clear set of rules to determine the appropriate action for each feedback item (implement, reject, defer, clarify).
  • Structured Responses: Generates clear, concise summaries of actions taken, reasons for rejection, and any necessary clarifications.
  • Use Case: When receiving a pull request review from another AI or a human, use this skill to systematically process each comment, ensuring that only valid and necessary changes are made.

Quick Start

Use the receive-feedback skill to process the latest code review comments.

Frequently Asked Questions about receive-feedback

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

FAQPage Schema
How do I process pull request feedback without blindly implementing every review comment?▼

Process pull request feedback by verifying technical claims against the codebase first, then applying a decision matrix to implement, reject, defer, or clarify each comment for rigorous code review handling.

What is the best way to evaluate LLM-generated code reviews for technical accuracy?▼

Evaluate LLM-generated code reviews using a verification-first methodology that checks feedback claims against existing codebase patterns before any implementation, ensuring technical accuracy over performative agreement.

How do I systematically handle CI linter outputs and human PR comments together?▼

Systematically handle CI linter outputs and human PR comments by passing them through a decision matrix that verifies each item against the codebase to determine whether to implement, reject, defer, or clarify.

Can I use this skill to reject code review feedback that does not match codebase patterns?▼

Yes, you can reject code review feedback that does not match codebase patterns by using the decision matrix to evaluate technical accuracy, generating structured responses with clear reasons for rejection or deferral.

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