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ClementWalter
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pr-review

Coordinate parallel agents to identify actionable issues in pull request changes.

Updated Aug 23, 2026
Search Tags:#automation#github#validation#code-review#parallel-agents#pr-review#batched-comments
One-click install
npx skills add https://github.com/ClementWalter/claudine --skill pr-review-clementwalter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pr-review
Source: https://github.com/ClementWalter/claudine/tree/main/.claude/skills/pr-review
Command: npx skills add https://github.com/ClementWalter/claudine --skill pr-review-clementwalter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ghapi, typer, rich.

What problem does it solve?

Perform thorough, constructive pull request reviews using parallel specialized agents. Use when you want to review a PR, provide code review feedback, or assess code changes. Features confidence-scored issues, validation filtering, and batched GitHub comments.

Core Features & Use Cases

  • Parallel, specialized agents coordinate to analyze PR changes
  • Confidence-scored issues with a validation step to surface high-signal findings
  • Batched GitHub comments to streamline review workflows and reduce noise
  • Clear, repeatable workflow for pre-review validation, context gathering, parallel analysis, and post-review actions

Quick Start

Run the CLI to fetch PR data, generate a batched review, and post it for a given PR.

Frequently Asked Questions about pr-review

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

FAQPage Schema
How do I automate GitHub pull request reviews with parallel agents?▼

Parallel PR review uses multiple specialized agents to analyze code changes simultaneously, validating findings against an 80% confidence threshold before posting batched comments to GitHub. This reduces noise and surfaces only high-signal, actionable issues.

How does confidence-scored validation filtering work for code review feedback?▼

Confidence-scored validation filtering enforces a minimum 80% certainty threshold on identified issues, ensuring only high-signal actionable findings pass through to the final batched GitHub comments during automated pull request code reviews.

Can I batch GitHub comments to reduce noise in automated PR reviews?▼

Yes, you can batch GitHub comments to streamline review workflows and reduce noise. The tool aggregates high-signal findings from parallel agents and posts them as batched comments rather than individual inline notifications on the pull request.

Does this parallel code review tool work with the GitHub API and Typer CLI?▼

Yes, the parallel code review tool integrates with the GitHub API via the ghapi dependency and uses Typer for its CLI interface, allowing you to fetch PR data, generate batched reviews, and post them directly from the command line.

What is the best way to check guideline compliance and error handling in pull requests?▼

The best way to check guideline compliance and error handling in pull requests is using parallel specialized agents that analyze code changes, target specific review scenarios, and surface high-signal findings through validated, batched GitHub comments.

Why should I use batched comments instead of individual inline PR comments?▼

Batched comments streamline review workflows and reduce notification noise by grouping high-signal findings into a single structured review, making it easier for developers to process actionable issues identified by parallel specialized agents.

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