GitHub Stars Research Analyzer

This skill provides in-depth analysis of GitHub repositories, tracking star growth, comparing popularity metrics, and generating research reports for open source projects and developer tools.

Capabilities

• Repository Analysis: Extract and analyze key metrics (stars, forks, issues, contributors, activity)
• Star Growth Tracking: Calculate daily, weekly, and monthly star growth rates and trends
• Comparative Analysis: Compare multiple repositories across various metrics
• Research Report Generation: Create comprehensive reports with insights and recommendations
• Data Visualization: Generate charts and graphs for trend analysis
• Export Formats: Output reports in Markdown, PDF, and JSON formats

Input Requirements

GitHub repository data can be provided in multiple formats:

• Repository URLs: Direct GitHub repository links
• Owner/Repo Names: GitHub owner and repository names
• JSON Input: Structured data with repository information
• CSV Lists: Multiple repositories in CSV format

Required fields:

• Repository owner (username or organization)
• Repository name
• Optional: Time period for analysis (default: last 30 days)

Output Formats

Results include:

• Metrics Summary: Key statistics and calculations
• Growth Analysis: Star growth rates and trends
• Comparative Insights: Multi-repository comparisons
• Visualizations: Charts and graphs (when applicable)
• Recommendations: Actionable insights for project maintainers
• Export Options: Markdown, PDF, and JSON reports

How to Use

“Analyze the GitHub repository claude-code-skills-factory and generate a star growth report” “Compare the popularity of these three repositories over the last 90 days” “Track star growth trends for the Anthropic organization’s repositories” “Generate a comprehensive research report on React’s star growth patterns”

Scripts

• github_api.py: Handles GitHub API interactions and data fetching
• analyze_repository.py: Core analysis engine for repository metrics
• generate_reports.py: Creates research reports in multiple formats
• visualize_data.py: Generates charts and visualizations

Best Practices

1. Respect Rate Limits: Always handle GitHub API rate limits gracefully
2. Data Validation: Verify repository existence and accessibility
3. Time Period Selection: Use appropriate time windows for meaningful analysis
4. Comparative Context: Always provide industry/ecosystem context for metrics
5. Privacy Considerations: Respect private repositories and user privacy

Limitations

• API Rate Limits: GitHub API has strict rate limits (60 requests/hour unauthenticated)
• Historical Data: Limited historical data availability through API
• Private Repositories: Cannot access private repositories without proper authentication
• Data Freshness: Real-time data depends on GitHub API updates
• Repository Age: New repositories may not have sufficient historical data