dev-final-pass

Review lesson directories for PR publication readiness with standardized quality checks.

Updated Mar 17, 2026
One-click install
npx skills add https://github.com/code-review-benchmark/coderabbit_prs2__forge-gpu__coderabbit__PR323__20260317 --skill dev-final-pass
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dev-final-pass
Source: https://github.com/code-review-benchmark/coderabbit_prs2__forge-gpu__coderabbit__PR323__20260317/tree/main/.claude/skills/dev-final-pass
Command: npx skills add https://github.com/code-review-benchmark/coderabbit_prs2__forge-gpu__coderabbit__PR323__20260317 --skill dev-final-pass

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Run a systematic quality review on any lesson (GPU, math, engine, UI, physics, audio, or asset pipeline) before creating a PR with /dev-create-pr. This skill encodes recurring themes from PR review feedback across the project's history.

Core Features & Use Cases

  • Applies a standardized, end-to-end set of checks to lessons across tracks (GPU, math, engine, UI, physics, audio, asset pipeline)
  • Ensures required files, coding conventions, resource cleanup, and documentation accuracy are present
  • Generates a concise, actionable summary of pass/fail per section to guide PR remediation

Quick Start

Run the dev-final-pass skill on the current lesson directory to perform the PR quality checks.

Frequently Asked Questions about dev-final-pass

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

FAQPage Schema
How do I run a quality review on a lesson pull request before publishing?

Run a systematic quality review on your lesson directory to identify publication-blocking issues. It applies standardized checks across GPU, math, engine, UI, physics, audio, and asset pipeline tracks to enforce coding conventions and required files.

What coding conventions are checked during a lesson PR quality review?

A lesson PR quality review checks for explicit required files, coding conventions, resource cleanup, naming standards, and documentation accuracy. These checks are derived from recurring themes in the project's historical pull request review feedback.

Can I use an automated code review for GPU and physics lesson tracks?

Yes, you can apply an automated code review to GPU and physics lesson tracks. The review scope covers multiple lesson types, ensuring resource cleanup and naming conventions are met before you create a pull request.

What's the best way to ensure documentation accuracy in a lesson code review?

The best way to ensure documentation accuracy is running a final quality pass that validates docs against project standards. It cross-references requirements defined by historical PR feedback to catch recurring documentation errors.

Why does my lesson pull request fail the final quality pass?

Your lesson pull request fails the final quality pass due to missing required files, improper naming, incomplete resource cleanup, or inaccurate documentation. The review generates a concise, actionable summary of pass/fail results per section to guide remediation.

Does the lesson quality review support C code and asset pipeline tracks?

Yes, the lesson quality review supports C code and asset pipeline tracks. It applies a standardized set of end-to-end checks to these tracks, enforcing coding conventions and required files as defined by project PR history.