This guide provides step-by-step instructions for setting up an automated workflow to scan your GitHub starred repositories, generate AI-powered descriptions, and organize them with relevant keywords and use cases.

Overview

The Starred Repository Scanner helps you:

  • Fetch all repositories you’ve starred on GitHub
  • Generate concise, AI-enhanced descriptions for each repository
  • Extract keywords and potential use cases
  • Organize repositories into categories
  • Output structured data for easy filtering and discovery

Prerequisites

Before starting, ensure you have:

  1. GitHub Account: With starred repositories to analyze
  2. API Access: Either GitHub MCP server or GitHub Personal Access Token
  3. AI Tool Access: GitHub Copilot, Claude, or similar AI assistant
  4. Python 3.8+: For running the scanner script (optional)
  5. Git: For cloning and version control

Step 1: List Your Starred Repositories

Use the github-mcp-server-list_starred_repositories tool to fetch your starred repositories:

// Fetch starred repositories with pagination
const stars = await list_starred_repositories({
  perPage: 100,
  page: 1,
  sort: "updated", // Sort by most recently updated
  direction: "desc"
});

Parameters:

  • username (optional): Target username (defaults to authenticated user)
  • perPage (optional): Results per page (max 100)
  • page (optional): Page number for pagination
  • sort (optional): Sort by “created” or “updated”
  • direction (optional): “asc” or “desc”

Step 2: Extract Repository Metadata

For each starred repository, collect:

  • Repository name and owner
  • Description
  • Primary language
  • Topics/tags
  • Star count
  • Last update timestamp

Step 3: Fetch Detailed Information

For repositories that need deeper analysis:

// Get repository README
const readme = await get_file_contents({
  owner: repo.owner,
  repo: repo.name,
  path: "README.md"
});

// Get repository topics
const repoDetails = await get_repository({
  owner: repo.owner,
  repo: repo.name
});

Step 4: Generate AI Analysis

Use the Repository Analyzer prompt to analyze each repository:

  1. Prepare repository information: 
    Repository: owner/repo-name
Description: [from GitHub]
Language: [primary language]
Topics: [comma-separated topics]
Stars: [star count]
README Summary: [first few sections]

  2. Submit to AI with the Repository Analyzer prompt

  3. Receive structured JSON output with:
    • Enhanced description
    • Keywords/tags
    • Use cases
    • Classification
    • Integration opportunities

Step 5: Aggregate and Store Results

Compile all analyzed repositories into a structured format:

{
  "scan_date": "2024-01-15T10:30:00Z",
  "total_repositories": 150,
  "repositories": [
    {
      "repository": "owner/repo-name",
      "github_url": "https://github.com/owner/repo-name",
      "stars": 1234,
      "language": "Python",
      "topics": ["machine-learning", "nlp"],
      "ai_description": "...",
      "keywords": ["..."],
      "use_cases": [...],
      "classification": {...}
    }
  ]
}

Method 2: Using GitHub REST API

Step 1: Generate Personal Access Token

  1. Go to GitHub Settings → Developer settings → Personal access tokens
  2. Generate new token with public_repo scope
  3. Save token securely

Step 2: Fetch Starred Repositories 

# Using curl
curl -H "Authorization: token YOUR_TOKEN" \
  https://api.github.com/user/starred?per_page=100&page=1

# Using Python
import requests

headers = {"Authorization": f"token {GITHUB_TOKEN}"}
response = requests.get(
    "https://api.github.com/user/starred",
    headers=headers,
    params={"per_page": 100, "page": 1}
)
stars = response.json()

Step 3: Process Each Repository

For each repository in the response:

  1. Extract metadata
  2. Fetch README content (if needed)
  3. Apply AI analysis
  4. Store results

Output Formats

Best for programmatic access and filtering:

{
  "repositories": [...]
}

Markdown Table Format

Best for human-readable summaries:

| Repository | Description | Keywords | Use Cases | Category |
|------------|-------------|----------|-----------|----------|
| owner/repo | AI-generated description | tag1, tag2 | Use case summary | Category |

CSV Format

Best for spreadsheet analysis:

repository,description,keywords,primary_category,stars,language
owner/repo,"Description","tag1,tag2",Category,1234,Python

Automation Options

Option 1: GitHub Actions Workflow

Create .github/workflows/scan-starred-repos.yml:

name: Scan Starred Repositories

on:
  schedule:
    - cron: '0 0 * * 0'  # Weekly on Sunday
  workflow_dispatch:  # Manual trigger

jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      
      - name: Install dependencies
        run: pip install requests
      
      - name: Scan starred repositories
        env:
          GITHUB_TOKEN: $
        run: python scripts/scan_starred_repos.py
      
      - name: Commit results
        run: |
          git config user.name github-actions
          git config user.email github-actions@github.com
          git add data/starred-repos.json
          git commit -m "Update starred repositories scan"
          git push

Option 2: Local Script

Run the scanner manually on your local machine:

# Install dependencies
pip install requests

# Run scanner
python scripts/scan_starred_repos.py --output data/starred-repos.json

# Review results
cat data/starred-repos.json | jq '.repositories[0]'

Option 3: Interactive Analysis

Use AI assistant interactively:

  1. Manually fetch starred repositories list
  2. Select repositories to analyze
  3. Use Repository Analyzer prompt for each
  4. Compile results manually or with simple script

Best Practices

Efficiency

  • Batch Processing: Analyze multiple repositories in one session
  • Caching: Save intermediate results to avoid re-fetching
  • Rate Limiting: Respect GitHub API rate limits (5000 req/hour for authenticated)
  • Incremental Updates: Only analyze new stars since last scan

Quality

  • Verify AI Output: Review AI-generated descriptions for accuracy
  • Manual Review: Flag repositories that need human verification
  • Consistent Format: Use structured JSON for easy parsing
  • Metadata Preservation: Keep original GitHub data alongside AI analysis

Organization

  • Categorization: Group repositories by primary category
  • Tagging: Use consistent keyword taxonomy
  • Priority: Mark high-priority repositories to explore
  • Notes: Add personal notes and learning goals

Use Cases

Personal Knowledge Management

  • Build a searchable database of tools and libraries
  • Track technologies you want to learn
  • Document how you’ve used specific repositories

Project Planning

  • Identify tools for new projects
  • Find alternatives to existing solutions
  • Discover integration opportunities

Learning and Development

  • Organize learning resources by topic
  • Track progress through technologies
  • Build curriculum from starred repositories

Team Collaboration

  • Share curated tool recommendations
  • Document team’s technology stack
  • Onboard new team members with organized resources

Troubleshooting

API Rate Limits

Problem: Hitting GitHub API rate limits
Solution: Use authentication, reduce frequency, or implement exponential backoff

Large Repository Sets

Problem: Too many repositories to process
Solution: Filter by language, recency, or star count; process in batches

AI Context Limits

Problem: README too large for AI context
Solution: Extract only key sections (first few paragraphs, features, usage)

Inconsistent Results

Problem: AI generates different formats
Solution: Use structured prompt with explicit JSON schema requirement

Future Enhancements

Planned improvements for this workflow:

  • Advanced Clustering: Group similar repositories using ML
  • Trend Analysis: Identify trending topics in your stars
  • Duplicate Detection: Find similar/alternative tools
  • Quality Scoring: Rank repositories by various metrics
  • Export Formats: Additional output formats (HTML, PDF, etc.)
  • Web Interface: Browser-based visualization and filtering

Ready to start scanning?

  1. Choose your method (MCP Server or REST API)
  2. Prepare your environment
  3. Run the scanner
  4. Review and organize results
  5. Set up automation for regular updates