github-stars-analyzer

Analyze GitHub repository metrics to generate star-growth reports in Markdown, JSON, and PDF.

3|Updated Oct 15, 2025
One-click install
npx skills add https://github.com/chaorenex1/coding-agent-workflow --skill github-stars-analyzer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: github-stars-analyzer
Source: https://github.com/chaorenex1/coding-agent-workflow/tree/main/skills/github-stars-analyzer
Command: npx skills add https://github.com/chaorenex1/coding-agent-workflow --skill github-stars-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, matplotlib, numpy, pyyaml.

What problem does it solve?

This skill analyzes GitHub repositories to track star growth, compare popularity, and generate research-style reports for open source projects.

Core Features & Use Cases

  • Repository Analysis: Metrics like stars, forks, issues, contributors, activity.
  • Star Growth Tracking: Daily/weekly/monthly star growth and trends.
  • Comparative Analysis: Compare multiple repositories across metrics.
  • Research Report Generation: Create reports with insights and recommendations.
  • Visualizations: Charts and graphs to illustrate trends.
  • Export Formats: Markdown, JSON (and PDFs in extended flows).

Quick Start

Analyze a single repository or compare several to produce a research report.

Frequently Asked Questions about github-stars-analyzer

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

FAQPage Schema
How do I track GitHub star growth and trends for a repository?

Star growth tracking analyzes daily, weekly, and monthly star accumulation patterns on GitHub repositories. This skill fetches repository metrics including stars, forks, watchers, and activity data, then generates trend reports with growth projections and visualizations to show how a project's popularity is changing over time.

Can I compare multiple GitHub repositories across metrics?

Comparative analysis lets you evaluate several repositories side-by-side using metrics like stars, forks, issues, contributors, and language distributions. The skill produces comparative insights and visualizations that highlight performance differences, making it easy to benchmark open source projects against each other.

What output formats does GitHub repository analysis support?

Repository analysis exports results in Markdown, JSON, and PDF formats. These formats suit different workflows: Markdown for documentation, JSON for programmatic use, and PDF for polished research-style reports with charts and insights ready for sharing.

What GitHub data do I need to provide for analysis?

Repository analysis requires core metrics: stars, forks, watchers, issues, contributors, and activity. Optional inputs include analysis period in days and comparison repository lists. The skill uses this data to calculate growth rates, project trends, and comparative rankings across repositories.

How does this work for organization-wide growth tracking?

Organization-wide tracking aggregates metrics across multiple repositories to reveal portfolio-level trends. By analyzing activity, language distributions, and star patterns across an organization's projects, you get comprehensive insights into collective growth and project health.

What's the best way to generate research reports from GitHub repository data?

Research-style reports combine metrics summaries, growth projections, visualizations, and comparative insights into exportable documents. The skill automates report generation with charts illustrating trends and recommendations, eliminating manual compilation and ensuring consistency across analyses.