Sac-Y

Sac-Y / Github-Quality

Public

An agent skill that evaluates any GitHub repo's true quality through a three-layer scoring system.

25
1
100% credibility
Found Apr 05, 2026 at 25 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A skill for AI agents that scores GitHub repositories on a 0-100 scale across objective metrics, engineering quality, and community reputation to help users gauge true value.

How It Works

1
🔍 Discover a GitHub project

You come across an open-source project online and wonder if it's truly solid and worth your time.

2
🤖 Ask your AI helper

Chat with your friendly AI assistant and say you want to check the project's quality.

3
📦 Add the quality tool

Your AI grabs and sets up the special checker tool with a simple request.

4
🔗 Connect your GitHub

One quick setup: link your GitHub login so the tool can peek at project details safely.

5
🎯 Start the check

Share the project's web link and ask for a full quality review – watch the magic happen!

6
⏳ AI reviews everything

Your AI examines popularity, organization, upkeep, and community buzz behind the scenes.

📊 See the score and advice

You get a clear 0-100 score report with breakdowns and tips on whether to use it.

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Star Growth

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AI-Generated Review

What is Github-Quality?

Github-Quality is an agent skill that rates any GitHub repo's true quality via a three-layer score (0-100): objective metrics like star growth and issue close rates (40%), engineering signals like README depth and CI/CD setup (40%), and community rep from HN/Twitter (20%). Drop a repo URL into your agent github claude, agent github code, or Cursor setup, and it runs shell queries via GitHub CLI for a report with recommendations. It cuts through star hype to reveal if a repo merits your time, perfect for agent skills library users.

Why is it gaining traction?

It goes beyond basic github code quality checks by blending GitHub data with external signals like Twitter discussions (via xreach) and HN sentiment, unlike simple star counters. The slash command trigger in agent github copilot vscode or claude integrations makes it dead simple, and weighted layers highlight real maintenance health. Devs dig the structured output for quick decisions on agent skills github or agent github repo evals.

Who should use this?

Dependency managers auditing open-source libs before npm installs, AI agent builders scanning agent skills anthropic or agent github action candidates, and tech leads vetting forks in agent github copilot intellij workflows.

Verdict

At 25 stars and 1.0% credibility score, it's immature with light testing and Twitter reliance, but bilingual docs and MIT license make it low-risk to try. Grab it if you're deep in agent skills io—saves hours on dud repo hunts.

(178 words)

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