review-intake

Validates code review feedback against the codebase using scout and grep.

82|23|Updated Feb 24, 2026
One-click install
npx skills add https://github.com/Rune-kit/rune --skill review-intake
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: review-intake
Source: https://github.com/Rune-kit/rune/tree/main/skills/review-intake
Command: npx skills add https://github.com/Rune-kit/rune --skill review-intake

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill prevents the costly mistake of blindly implementing code review feedback or external suggestions, ensuring changes are verified against the codebase before implementation.

Core Features & Use Cases

  • Feedback Verification: Ensures reviewer claims are accurate and applicable to the current codebase.
  • Prioritization: Classifies feedback (blocking, bug, improvement, style) for efficient handling.
  • Use Case: When you receive a PR comment suggesting a code change, use this skill to automatically check if the suggested change would break existing tests or conflict with architectural decisions before you even start coding.

Quick Start

Process the latest code review feedback by invoking the review-intake skill.

Frequently Asked Questions about review-intake

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

FAQPage Schema
How do I validate code review feedback before implementing changes?

To validate code review feedback, use an automated intake process that verifies reviewer claims against your codebase. This ensures suggestions are checked for accuracy, scope, and potential regressions before any implementation begins.

What is the best way to prioritize PR comments by severity?

Prioritizing PR comments involves classifying feedback into categories like blocking issues, bugs, improvements, and style. This allows you to address critical regressions first before moving to minor stylistic suggestions.

How do I prevent blind implementation of incorrect code review suggestions?

Preventing blind implementation requires a verification-first discipline that checks external suggestions against the existing codebase. This stops incorrect or out-of-scope reviewer claims from introducing new regressions.

Can I verify reviewer claims against my codebase automatically?

Yes, you can verify reviewer claims automatically by analyzing feedback and using search tools like grep to check the codebase. This confirms whether the suggested changes are applicable to the current architecture.

How do I check if a code review suggestion will break existing tests?

Checking if a code review suggestion breaks existing tests requires validating the feedback against your current codebase. This verification-first approach identifies potential regressions before you start coding.

When should I use an automated feedback verification process for pull requests?

You should use an automated feedback verification process when you receive PR comments suggesting code changes. It enforces a verification-first discipline, ensuring external suggestions are accurate before implementation.