peer-review

Verify external code-review findings against the actual codebase.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/jbrowning24/NestCheck --skill peer-review-jbrowning24
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: peer-review
Source: https://github.com/jbrowning24/NestCheck/tree/main/.agents/skills/peer-review
Command: npx skills add https://github.com/jbrowning24/NestCheck --skill peer-review-jbrowning24

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Critically evaluate code review feedback from another AI model or reviewer. Use this skill when someone pastes review findings from another model (GPT, Gemini, Copilot, etc.) or another reviewer and wants you to verify whether the findings are real. Also trigger on phrases like "peer review," "check this feedback," "another model found these issues," "evaluate this review," or "here's what [model] said." This skill prevents blindly applying bad suggestions by treating every finding as a hypothesis to verify against the actual code.

Core Features & Use Cases

  • Structured evaluation: Verify each finding against the actual code and context.
  • Context-aware decision-making: Distinguish real issues from false positives or intentional patterns.
  • Actionable remediation guidance: Provide prioritized fixes and rationale based on project goals and constraints.
  • Use Case: When a reviewer flags potential issues, verify their existence by inspecting the relevant files, dependencies, and surrounding code to determine if the finding is valid.

Quick Start

Verify a provided review finding against the actual codebase to validate its accuracy.

Frequently Asked Questions about peer-review

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

FAQPage Schema
How do I verify AI code review findings against my actual codebase?

Verify AI code review findings by treating each suggestion as a hypothesis and cross-checking it against the actual codebase, project context, and decisions to classify accuracy and guide remediation.

What is the best way to triage false positives from automated code review feedback?

Triage false positives from automated code review feedback by distinguishing real issues from intentional patterns through context-aware decision-making and inspecting relevant files and dependencies.

How do I check code review feedback from another model for accuracy?

Check code review feedback from another model for accuracy by cross-checking the flagged issues against the surrounding code, dependencies, and project constraints to confirm validity.

Can I evaluate suggestions from external reviewers without blindly applying them?

You can evaluate suggestions from external reviewers without blindly applying them by structurally verifying each finding against the actual code and context to determine if the finding is valid.

When do I need to cross-check external code review findings?

You need to cross-check external code review findings when feedback comes from other models or reviewers and you need to confirm accuracy before applying changes to the codebase.