thedaviddias/Front-End-Checklist

🗂 The essential checklist for modern web development, for humans and AI agents

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Summary Information

Updated 19 minutes ago
Added to GitGenius on June 9th, 2026
Created on October 16th, 2017
Open Issues & Pull Requests: 11 (+0)
GitHub issues: Enabled
Number of forks: 6,733
Total Stargazers: 74,105 (+0)
Total Subscribers: 1,430 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.6 days
Mean response time: 11.3 days
90th percentile: 38.6 days
Tracked items: 7

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 11% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 6
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 10 days
Stale 30+ days: 3
Stale 90+ days: 2

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • stale (7)
  • wontfix (6)

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

Front-End Checklist is a quality assurance system that organizes front-end best practices into an actionable checklist for web developers and AI agents.

The tool addresses the challenge of maintaining consistent quality across front-end projects by consolidating best practices into a structured, prioritized ruleset. It works by presenting rules across multiple categories with severity levels that distinguish between site-breaking issues, major user experience impacts, strong best practices, and situational improvements. Each rule includes explanations, remediation guidance, and verification steps. The checklist is accessible through three interfaces: an interactive website with filtering and category navigation, a Model Context Protocol server for integration with AI agents, and the README itself for quick reference.

Developers should adopt this tool if they need a systematic way to audit front-end quality before launch or during code review. It suits projects of any size that want to ensure consistency across HTML, CSS, JavaScript, accessibility, performance, SEO, and security concerns. The tool is particularly valuable for teams using AI agents in their workflows, since the MCP server exposes the same rule corpus that agents can query directly. The README recommends using the website for interactive browsing, the MCP server when agents need to access rules, and the README when you want everything in one place. A companion project, UX Patterns for Devs, helps developers choose the right UI pattern before using Front-End Checklist to verify implementation quality.

The project maintains an organized rule structure with validation and scoring tooling built into the development workflow. Derived artifacts are regenerated to keep documentation synchronized with the underlying rule corpus.