review

Analyze pull request diffs for SQL safety and data integrity risks.

Updated Mar 21, 2026
One-click install
npx skills add https://github.com/TimHL5/carousel --skill review-timhl5
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: review
Source: https://github.com/TimHL5/carousel/tree/main/.claude/skills/gstack/review
Command: npx skills add https://github.com/TimHL5/carousel --skill review-timhl5

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pre-landing PR review analyzes diffs against the base branch to surface SQL safety issues, LLM trust boundary violations, conditional side effects, and other structural risks before merging.

Core Features & Use Cases

  • Analyze diffs to surface SQL safety and data-integrity risks.
  • Detect LLM trust boundary violations and conditional side effects that could alter behavior post-merge.
  • Provide actionable recommendations and proactive merge-time guidance to improve code quality.

Quick Start

Provide a concise PR review by analyzing the current diff against the base branch and surfacing critical issues and actionable recommendations.

Frequently Asked Questions about review

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

FAQPage Schema
How do I automate pull request reviews for SQL safety issues?

Automated pull request review analyzes diffs against the base branch to surface SQL safety issues and data integrity risks. It provides actionable recommendations and proactive merge-time guidance to guard code quality before landing changes.

What are LLM trust boundary violations and how can I detect them in a diff?

LLM trust boundary violations are structural risks where model inputs or outputs bypass safety constraints. Diff analysis detects these violations by examining pull request changes to identify conditional side effects that could alter behavior post-merge.

Can I check for data integrity risks before merging a pull request?

Yes, pre-landing PR review checks for data integrity risks by analyzing pull request diffs against the base branch. It surfaces critical SQL safety issues and conditional side effects, providing actionable recommendations to improve code quality before merging.

How does automated diff analysis handle conditional side effects in code?

Automated diff analysis identifies conditional side effects that could alter behavior post-merge by examining pull request changes against the base branch. It surfaces these structural risks alongside SQL safety issues to provide proactive merge-time suggestions.

What is the best way to get proactive merge-time suggestions for code quality?

The best way to get proactive merge-time suggestions is through automated diff analysis that surfaces SQL safety issues, LLM trust boundary violations, and conditional side effects. This provides actionable recommendations to guard quality and safety before merging.