Detect React codebase quality regressions and return a numeric health score.
npx skills add https://github.com/Makisuo/maple --skill react-doctor-makisuo
Please help me install this Agent Skill. Skill: react-doctor Source: https://github.com/Makisuo/maple/tree/main/.agents/skills/react-doctor Command: npx skills add https://github.com/Makisuo/maple --skill react-doctor-makisuo
## What problem does it solve? Quickly identify and prevent quality regressions in React codebases by providing automated health checks that flag issues before they reach production.
## Core Features & Use Cases
### Quick Start Run a fast scan of the current project with the latest React Doctor analyzer to surface issues and a health score.
High-intent search queries and answers about installing and using this skill.
You can check React codebase quality by running an automated scan that surfaces lint issues, dead code, accessibility gaps, bundle-size bloat, and architecture concerns. This scan returns a 0–100 health score to quantify code quality and guide refactoring efforts before changes are committed.
A React health score is a numeric value from 0–100 that quantifies code quality based on lint rule adherence, dead code, accessibility gaps, bundle-size bloat, and architecture diagnostics. It provides a measurable benchmark to track maintainability and guide refactoring efforts.
Yes, you can detect accessibility gaps and dead code in React feature branches by running an automated analyzer. The scan performs accessibility auditing and dead-code detection alongside lint and architecture checks to surface issues before they reach production.
You can evaluate React bundle-size bloat and architectural concerns automatically by running a diagnostic scan. The analyzer performs bundle-size evaluation and architecture diagnostics on your codebase to prevent performance regressions and structural decay.
Yes, the automated React code quality scan is applicable to feature branches, PRs, and pre-commit checks. It surfaces lint issues, dead code, accessibility gaps, and bundle-size bloat to ensure changes meet quality standards and avoid regressions before production.
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