Phase 1: Detailed Candidate Identification Plan

# Phase 1: Detailed Candidate Identification Plan

**Parent:** [Project Design](../../PROJECT_DESIGN.md)
**Related:** [Project Selection Methodology](README.md) - 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:**
```bash
# 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](../../projects/01-project-selection/github-api-collector/README.md) - Execute all GitHub API searches
- [Trending Analyzer](../../projects/01-project-selection/trending-analyzer/README.md) - Analyze trending repositories
- [Metrics Calculator](../../projects/01-project-selection/metrics-calculator/README.md) - Calculate quantitative metrics
- [Package Manager Collector](../../projects/01-project-selection/package-manager-collector/README.md) - Collect package ecosystem data
- [Pattern Matcher](../../projects/01-project-selection/pattern-matcher/README.md) - 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.