Getting Started with Starred Repository Scanner

A quick guide to scanning and analyzing your GitHub starred repositories with AI-powered insights.

What is the Starred Repository Scanner?

The Starred Repository Scanner is an automated workflow that helps you:

  • Fetch all repositories you’ve starred on GitHub
  • Generate AI-enhanced descriptions for each repository
  • Extract keywords and potential use cases
  • Organize repositories by language, category, and use case
  • Export structured data for easy searching and filtering

Quick Start (5 Minutes)

Prerequisites

  • Python 3.8 or higher
  • GitHub account with starred repositories
  • GitHub Personal Access Token (optional but recommended)

Step 1: Install Dependencies 

pip install requests

That’s it! Only one dependency needed.

Step 2: Get GitHub Token (Optional)

For scanning your own starred repositories:

  1. Go to https://github.com/settings/tokens
  2. Click “Generate new token (classic)”
  3. Select scope: public_repo or repo
  4. Copy the token

Step 3: Run Your First Scan 

# Set your token (recommended)
export GITHUB_TOKEN="your_token_here"

# Basic scan
python scripts/scan_starred_repos.py --output data/starred-repos.json --limit 10

# OR with enhanced descriptions (NEW!)
python scripts/scan_starred_repos.py --enhance-description --limit 10

# OR with full analysis (languages + enhanced descriptions)
python scripts/scan_starred_repos.py --include-languages --enhance-description --limit 10

This will scan your first 10 starred repositories and save the results.

New Features:

  • --enhance-description: Automatically generates rich descriptions from metadata
  • --include-languages: Fetches language breakdown with percentages
  • --include-readme: Includes README previews for deeper AI analysis

Step 4: Review the Output 

# View the results
cat data/starred-repos.json | python -m json.tool | head -50

# Or open in your favorite editor
code data/starred-repos.json

You’ll see structured data like:

{
  "scan_date": "2024-01-15T10:30:00Z",
  "total_repositories": 10,
  "repositories": [
    {
      "repository": "owner/repo-name",
      "description": "Repository description",
      "enhanced_description": "Repository description | Topics: web, api | Built with Python | Popular project with 2,345 stars",
      "language": "Python",
      "languages": {
        "Python": 85.5,
        "JavaScript": 10.2,
        "CSS": 4.3
      },
      "topics": ["topic1", "topic2"],
      "stars": 1234,
      ...
    }
  ]
}

Step 5: Analyze with AI

Now use the Repository Analyzer prompt to enhance each repository:

  1. Open your AI assistant (GitHub Copilot, Claude, ChatGPT, etc.)
  2. Copy the Repository Analyzer prompt
  3. For each repository, provide its data
  4. Get enhanced descriptions, keywords, and use cases

Example input to AI:

Repository: facebook/react
Description: "The library for web and native user interfaces"
Language: JavaScript
Topics: javascript, react, frontend, ui
Stars: 225,000

Example output from AI:

  • Enhanced Description: “React is a declarative, component-based JavaScript library…”
  • Keywords: javascript-library, ui-framework, component-based, …
  • Use Cases: Building Modern Web Applications, Cross-Platform Mobile Development, …

Step 6: Organize Your Results

Build a searchable database:

  • Group by language
  • Filter by use case
  • Search by keywords
  • Track learning priorities

See example analysis for inspiration.

Common Commands 

# Scan all your starred repos
python scripts/scan_starred_repos.py --output data/starred-repos.json

# NEW: Scan with enhanced descriptions
python scripts/scan_starred_repos.py --enhance-description --output data/starred-repos.json

# NEW: Comprehensive scan with all features
python scripts/scan_starred_repos.py --enhance-description --include-languages --include-readme --limit 50

# Scan with README previews (slower but more context)
python scripts/scan_starred_repos.py --include-readme --limit 20

# Scan another user's public stars
python scripts/scan_starred_repos.py --username torvalds --limit 50

# Quick test with first 5 repos
python scripts/scan_starred_repos.py --limit 5

Next Steps

  1. Full Scan: Remove --limit to scan all your stars
  2. AI Analysis: Use the prompt to analyze repositories in batches
  3. Automation: Set up GitHub Actions for weekly scans (see workflow example)
  4. Organization: Build your personal repository knowledge base
  5. Sharing: Share curated lists with your team

Troubleshooting

“Authentication required”

  • Set GITHUB_TOKEN environment variable
  • Or use --token parameter

“API rate limit”

  • Use authentication (gives 5000 req/hour vs 60)
  • Add delays between requests
  • Scan in smaller batches

“Module not found: requests”

  • Install: pip install requests
  • Or: python -m pip install requests

Learn More

Features Overview

Automated Scanning

  • Fetch all starred repositories via GitHub API
  • Pagination support for large collections
  • Rate limit monitoring and handling
  • Comprehensive error handling

Enhanced Descriptions

  • Automatic generation from repository metadata
  • Combines description, topics, language, and popularity
  • README analysis for deeper insights
  • Multiple levels: brief, standard, and detailed

Language Analysis

  • Percentage breakdown of languages used
  • Understand polyglot projects
  • Tech stack composition at a glance

AI-Powered Analysis

  • Multi-level description generation
  • Keyword extraction (8-15 relevant items)
  • Use case identification with examples
  • Technical assessment and comparisons
  • Integration opportunities

Flexible Output

  • Structured JSON format
  • Machine-readable and human-friendly
  • Easy filtering and searching
  • Export to various formats

Happy scanning! 🚀