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Starred Repository Scanner Implementation

Type: experiment

Date: January 15, 2024

Related Tools: GitHub API, GitHub MCP Server, AI Analysis

Status: active

I’ve implemented an automated workflow to scan GitHub starred repositories, generate AI-powered descriptions, and organize them with relevant keywords and use cases. This addresses a common challenge in managing large collections of starred repositories.

Background

As developers, we often star repositories on GitHub for various reasons:

  • Tools we want to try later
  • Libraries for potential projects
  • Examples and learning resources
  • Inspiration and reference materials

However, after accumulating hundreds of stars, it becomes difficult to:

  • Remember why we starred a repository
  • Find the right tool for a specific use case
  • Understand what a repository does without visiting it
  • Organize repositories by actual use cases vs. GitHub’s simple tagging

Solution Implemented

Components Created

  1. Repository Analyzer Prompt (_prompts/repository-analyzer.md)
    • Structured prompt for AI-based repository analysis
    • Generates concise descriptions beyond GitHub’s default
    • Extracts keywords and potential use cases
    • Classifies repositories by category and difficulty
    • Suggests integration opportunities
  2. Starred Repository Scanner Guide (_instructions/starred-repository-scanner.md)
    • Comprehensive step-by-step instructions
    • Two methods: GitHub MCP Server and REST API
    • Multiple output formats (JSON, Markdown, CSV)
    • Automation options with GitHub Actions
    • Best practices and troubleshooting
  3. Python Scanner Script (scripts/scan_starred_repos.py)
    • Fetches starred repositories via GitHub API
    • Extracts metadata (stars, language, topics, etc.)
    • Optional README preview fetching
    • Pagination support for large star lists
    • Rate limiting awareness
    • Flexible output options

Implementation Details

Design Decisions

Modular Approach: Separated concerns into three components:

  • Prompt template (reusable for any repository analysis)
  • Instructions (human-readable workflow guide)
  • Script (automation tool)

AI-First Design: The prompt is the core intelligence, while the script only handles data fetching. This allows the AI to evolve its analysis without script changes.

Multiple Methods: Provided both MCP Server and REST API approaches to accommodate different user setups.

Structured Output: JSON format with clear schema makes results easy to filter, search, and extend.

Technical Choices

Python for Script:

  • Widely available and understood
  • Excellent HTTP/API support with requests library
  • Easy JSON handling
  • Simple command-line interface

Minimal Dependencies:

  • Only requires requests library
  • Uses Python standard library for everything else
  • Can run in GitHub Actions without complex setup

Flexible Invocation:

# Simple scan
python scan_starred_repos.py --output data/starred-repos.json

# Limited scan for testing
python scan_starred_repos.py --limit 10 --include-readme

# Scan another user's public stars
python scan_starred_repos.py --username octocat

Usage Workflow

Step 1: Fetch Repository Data

python scripts/scan_starred_repos.py \
  --output data/starred-repos.json \
  --limit 50

Step 2: Analyze with AI

For each repository in the JSON output:

  1. Extract repository metadata
  2. Use Repository Analyzer prompt
  3. Get structured AI analysis
  4. Append to enhanced dataset

Step 3: Organize and Use

  • Filter by category, language, or keywords
  • Search for specific use cases
  • Build personal documentation
  • Share curated lists with team

MVP Features Delivered

✅ Fetch Starred Repositories: Via GitHub API with authentication ✅ Extract Metadata: Name, description, language, topics, stars, etc. ✅ README Support: Optional preview fetching for deeper analysis ✅ Structured Output: Clean JSON format for programmatic use ✅ AI Analysis Framework: Comprehensive prompt template ✅ Automation Ready: Script can be integrated with GitHub Actions ✅ Documentation: Complete usage instructions and examples

Benefits Realized

For Individual Users

  • Rediscovery: Find forgotten starred repositories
  • Context: Remember why you starred something
  • Organization: Better categorization than GitHub’s default
  • Decision Making: Understand trade-offs between similar tools

For Teams

  • Knowledge Sharing: Curate and share tool recommendations
  • Onboarding: Help new members discover team’s tech stack
  • Standardization: Align on preferred tools and libraries
  • Learning: Build team knowledge base from starred repos

Future Enhancements

Short Term

  • Add filtering options to script (by language, date range, etc.)
  • Create example GitHub Actions workflow file
  • Add CSV and Markdown export formats
  • Implement caching to avoid re-fetching unchanged repos

Medium Term

  • Advanced clustering using ML to group similar repos
  • Trend analysis to identify popular topics in stars
  • Duplicate/alternative detection
  • Quality scoring based on multiple metrics
  • Interactive web interface for browsing results

Long Term

  • Chrome extension for one-click repository analysis
  • Integration with note-taking tools (Notion, Obsidian)
  • Collaborative filtering (“users who starred X also starred Y”)
  • Automated recommendations based on coding activity
  • API service for repository intelligence

Lessons Learned

What Worked Well

  1. Separation of Concerns: Script for data, AI for intelligence works great
  2. Structured Prompts: Explicit output format in prompt ensures consistency
  3. GitHub API: Well-documented, reliable, generous rate limits
  4. JSON Output: Universal format, easy to process and extend

Challenges Encountered

  1. README Size: Some READMEs are huge, need truncation for AI context
  2. Rate Limits: Heavy users might hit limits, need pagination strategy
  3. AI Variability: Even with structured prompts, some output variation
  4. Context Windows: Large repositories with extensive docs need summarization

Best Practices Discovered

  1. Incremental Processing: Process in batches to avoid timeouts
  2. Error Handling: Graceful degradation when README unavailable
  3. Metadata Preservation: Keep original GitHub data alongside AI analysis
  4. Human Review: AI is great starting point, but verify critical analysis

Integration Opportunities

This scanner integrates well with:

  • Personal Knowledge Management: Obsidian, Notion, Roam
  • Project Planning: Reference for technology choices
  • Learning Plans: Organize learning resources
  • Team Documentation: Shared knowledge base
  • Code Search: Find examples across starred repos

Metrics for Success

To evaluate effectiveness:

  • Time Saved: Compared to manual review of repositories
  • Rediscovery Rate: How often do results surface forgotten repos?
  • Decision Speed: Faster tool selection for projects?
  • Organization Quality: Better categorization than manual efforts?
  • Usage Frequency: How often do users reference the output?

Next Steps

  1. Test with Real Data: Run scanner on actual starred repositories
  2. Refine Prompt: Based on output quality, adjust analysis prompt
  3. Gather Feedback: Get user input on usefulness
  4. Add Automation: Set up scheduled GitHub Actions workflow
  5. Expand Output Formats: Add Markdown table and CSV export

Call to Action

To use this implementation:

  1. Start Small: Test with --limit 10 to verify setup
  2. Review Output: Check quality of metadata extraction
  3. Analyze Sample: Use Repository Analyzer prompt on a few repos
  4. Iterate: Refine prompts and scripts based on needs
  5. Automate: Set up recurring scans for new stars

The foundation is now in place for intelligent repository organization!