Phase 1: Detailed Candidate Identification Plan

Parent: Project Design Related: Project Selection Methodology - High-level selection approach

Overview

This document provides specific implementation details for identifying candidate projects across all 8 categories using multiple discovery methods. This is the detailed execution plan that implements the high-level methodology described in README.md.

Relationship to README.md:

• README.md: High-level strategy, criteria, and timeline
• CANDIDATE_IDENTIFICATION_PLAN.md: Detailed implementation steps and specific queries
• AUTOMATION_OPPORTUNITIES.md: Which tasks can be automated vs. require human judgment

Method 1: Knowledge-Based Selection

Training Data Mining

Implementation:

• Query knowledge base for "best practices" projects in each category
• Search for projects mentioned in architectural discussions
• Identify projects from "awesome lists" and curated collections
• Reference projects from educational materials and tutorials

Specific Queries to Execute:

• "What are the most well-regarded Chrome extensions for developers?"
• "Which Rust libraries are considered best-in-class for performance?"
• "What are the most popular MCP server implementations?"
• "Which full-stack frameworks are known for their architecture?"
• "What are the most respected data science libraries?"
• "Which CLI tools are considered gold standards?"
• "What mobile app frameworks are most recommended?"
• "Which documentation platforms are most praised?"

Expected Output: 5-10 candidates per category from knowledge base

Community Recognition

Implementation:

• Search for projects mentioned in "best practices" discussions
• Identify projects from Stack Overflow "most loved" technologies
• Reference projects from developer surveys and reports
• Find projects mentioned in conference talks and presentations

Specific Sources:

• Stack Overflow Developer Survey results
• State of JS, State of CSS, State of Rust surveys
• GitHub's "Trending" and "Most Starred" lists
• Conference talk abstracts and slides
• Blog posts about "best practices" and "architecture"

Expected Output: 3-5 additional candidates per category

Method 2: Search-Based Discovery

GitHub Trending Analysis

Implementation:

• Use GitHub API to query trending repositories by language/topic
• Filter by category-specific topics and tags
• Analyze trending over different time periods (weekly, monthly, yearly)
• Cross-reference with star growth rates

Specific GitHub Searches:

# Chrome Extensions
topic:chrome-extension
topic:browser-extension
language:JavaScript topic:extension

# MCP Servers
topic:mcp-server
topic:model-context-protocol
language:Python topic:mcp

# Rust Libraries
language:Rust
topic:rust-library
topic:crate

# Full-Stack Systems
topic:fullstack
topic:web-application
topic:api

# Data Science & ML
topic:machine-learning
topic:data-science
topic:ml
language:Python topic:data

# CLI Tools
topic:cli
topic:command-line
topic:terminal

# Mobile Applications
topic:mobile
topic:react-native
topic:flutter
topic:mobile-app

# Documentation Sites
topic:documentation
topic:docs
topic:docusaurus
topic:gitbook

Expected Output: 10-15 candidates per category

Star Count Analysis

Implementation:

• Query GitHub API for repositories sorted by star count
• Filter by category-specific criteria
• Analyze star-to-fork ratios for community engagement
• Cross-reference with recent activity

API Queries:

# Example GitHub API calls
curl "https://api.github.com/search/repositories?q=language:rust+stars:>1000&sort=stars&order=desc"
curl "https://api.github.com/search/repositories?q=topic:chrome-extension+stars:>500&sort=stars&order=desc"

Expected Output: 5-8 candidates per category

Fork Analysis

Implementation:

• Analyze fork-to-star ratios to identify community adoption
• Look for projects with high fork counts relative to stars
• Identify projects with active fork communities
• Cross-reference with recent fork activity

Metrics to Calculate:

• Fork-to-star ratio
• Recent fork activity (last 6 months)
• Fork engagement (issues, PRs from forks)
• Fork maintenance (active forks vs. stale forks)

Expected Output: 3-5 candidates per category

Method 3: Community-Based Research

Developer Survey Analysis

Implementation:

• Analyze Stack Overflow Developer Survey results
• Reference State of JS, State of CSS, State of Rust surveys
• Cross-reference with GitHub's "Most Loved" and "Most Wanted" lists
• Identify projects from "Tools & Technologies" sections

Specific Survey Sources:

• Stack Overflow Developer Survey 2023/2024
• State of JS 2023
• State of CSS 2023
• State of Rust 2023
• GitHub's "Most Loved" repositories
• Developer satisfaction surveys

Expected Output: 2-3 candidates per category

Industry Report Analysis

Implementation:

• Search for "best practices" and "architecture" blog posts
• Identify projects from conference presentations
• Reference projects from technical books and courses
• Analyze projects from "awesome lists" and curated collections

Specific Sources:

• Conference talk abstracts (JSConf, RustConf, PyCon, etc.)
• Technical blog posts about architecture
• "Awesome" lists on GitHub
• Technical book references
• Online course materials

Expected Output: 3-5 candidates per category

Method 4: Category-Specific Discovery

Chrome Extensions

Specific Searches:

• Chrome Web Store "Featured" and "Popular" sections
• GitHub topics: chrome-extension, browser-extension
• NPM packages with chrome-extension keyword
• Projects mentioned in Chrome extension development guides

Evaluation Criteria:

• Chrome Web Store ratings and reviews
• User count and active installations
• Security audit results
• Chrome API compatibility

MCP Servers

Specific Searches:

• GitHub topics: mcp-server, model-context-protocol
• Projects from MCP documentation and examples
• AI/ML community discussions about MCP
• Projects referenced in MCP tutorials

Evaluation Criteria:

• MCP protocol compliance
• AI model integration quality
• Documentation completeness
• Community adoption

Rust Libraries

Specific Searches:

• crates.io by download count and rating
• GitHub topics: rust-library, crate
• Projects from Rust "awesome" lists
• Projects mentioned in Rust community discussions

Evaluation Criteria:

• crates.io download statistics
• Reverse dependency count
• Performance benchmarks
• Memory safety practices

Full-Stack Systems

Specific Searches:

• GitHub topics: fullstack, web-application, api
• Projects from full-stack development guides
• Projects mentioned in architecture discussions
• Projects from deployment and scaling guides

Evaluation Criteria:

• Architecture complexity and quality
• Scalability practices
• Deployment strategies
• Monitoring and observability

Data Science & ML Projects

Specific Searches:

• PyPI packages by download count
• GitHub topics: machine-learning, data-science, ml
• Projects from ML community discussions
• Projects mentioned in data science tutorials

Evaluation Criteria:

• PyPI download statistics
• Academic citations
• Reproducibility practices
• Documentation quality

CLI Tools & Applications

Specific Searches:

• GitHub topics: cli, command-line, terminal
• Projects from CLI development guides
• Projects mentioned in developer productivity discussions
• Projects from terminal and shell guides

Evaluation Criteria:

• User experience and interface design
• Performance and resource usage
• Cross-platform compatibility
• Package manager adoption

Mobile Applications

Specific Searches:

• GitHub topics: mobile, react-native, flutter, mobile-app
• Projects from mobile development guides
• Projects mentioned in mobile architecture discussions
• Projects from app store listings

Evaluation Criteria:

• App store ratings and reviews
• User experience quality
• Performance optimization
• Platform compatibility

Documentation Sites

Specific Searches:

• GitHub topics: documentation, docs, docusaurus, gitbook
• Projects from documentation platform guides
• Projects mentioned in technical writing discussions
• Projects from content management guides

Evaluation Criteria:

• Content quality and accuracy
• User experience and navigation
• Accessibility compliance
• Community contribution

Implementation Timeline

Week 1: Automated Discovery (Fully Automated)

Automation Tools:

• GitHub API Collector - Execute all GitHub API searches
• Trending Analyzer - Analyze trending repositories
• Metrics Calculator - Calculate quantitative metrics
• Package Manager Collector - Collect package ecosystem data
• Pattern Matcher - Detect documentation and security files

Automated Tasks:

• Execute GitHub API searches for all categories
• Analyze trending repositories across time periods
• Calculate star growth rates, activity scores, community health metrics
• Collect NPM, PyPI, crates.io download statistics
• Detect presence of README, CONTRIBUTING, LICENSE files
• Detect security files (SECURITY.md, dependabot configs)
• Generate quantitative scoring reports
• Compile initial candidate lists with metrics

Week 2: Human Judgment & Selection

Human Tasks:

• Review automated results and metrics
• Apply qualitative assessment criteria
• Analyze developer surveys and community recognition
• Review conference presentations and industry reports
• Cross-reference with community discussions
• Apply diversity requirements (scale, approach, community)
• Make final selection decisions

Week 3: Final Selection & Documentation

Human Tasks:

• Final project selection and ranking
• Category representation assessment
• Selection rationale documentation
• Quality assurance review
• Handoff preparation for Phase 2

Expected Output

Target Numbers:

• 20-30 candidates per category (160-240 total)
• 5-10 from knowledge base
• 10-15 from GitHub trending
• 5-8 from star count analysis
• 3-5 from fork analysis
• 2-3 from developer surveys
• 3-5 from industry reports

Automation Benefits:

• 70% Token Reduction: From ~50,000 to ~15,000 tokens
• Faster Processing: Automated data collection vs. manual queries
• Consistent Metrics: Standardized quantitative analysis
• Human Focus: Judgment tasks only, not mechanistic data collection

Quality Assurance:

• All candidates meet minimum activity thresholds (automated)
• All candidates have comprehensive documentation (automated detection)
• All candidates demonstrate active community engagement (automated metrics)
• All candidates represent different scales and approaches (human judgment)

This detailed plan ensures systematic discovery of high-quality candidates across all categories while maintaining diversity in scale, approach, and community dynamics.