Tech Ecosystem Analyzer Skill

Overview

A comprehensive skill for data-driven analysis of technology ecosystems. Combines web research, GitHub API metrics, quantitative scoring, and professional documentation to help teams make informed technology selection decisions.

What This Skill Does

Transforms subjective technology selection into objective, metrics-based decision-making by:

1. Multi-angle web research - Gathers current trends, comparisons, and version updates
2. Automated GitHub data collection - Fetches stars, forks, growth rates, and activity metrics
3. Quantitative analysis - Calculates popularity scores and growth trajectories
4. Comprehensive reporting - Produces structured Markdown reports with actionable recommendations
5. Data preservation - Exports raw JSON data for further analysis

When to Use

Perfect for:

• Comparing multiple libraries/frameworks (React state management, testing tools, etc.)
• Technology stack evaluation for new projects
• Migration decisions (Redux → Zustand, etc.)
• Ecosystem trend monitoring
• Competitive technical analysis
• Open source project health assessment

Output

Each analysis produces three files:

1. [ecosystem]_analysis.md - Comprehensive report (typically 25-30 KB)

   • Executive summary with key findings
   • GitHub metrics rankings
   • Detailed library profiles
   • Comparative analysis tables
   • Top 3 recommendations with rationale
   • Trend predictions and scenarios
   • Actionable implementation guides
   • Learning resources

2. [ecosystem]_data.json - Raw metrics

   • Stars, forks, issues, watchers
   • Popularity scores
   • Growth rate calculations
   • All repository metadata

3. collect_github_data.py - Reusable script

   • Customizable for other ecosystems
   • Automated API fetching
   • Scoring algorithm implementation

Example Usage

React State Management Analysis

User: "Analyze React state management ecosystem: Redux, Zustand, Jotai, TanStack Query"

Output:
- 715-line comprehensive report
- Quantitative rankings (TanStack Query: 62.5/100, Zustand: 64.13/100, etc.)
- Growth predictions (Zustand to overtake Redux by 2026)
- Project-specific recommendations (startup vs enterprise)
- Migration strategies with ROI estimates

Python ML Libraries Comparison

User: "Compare Python ML libraries for a new data science project"

Output:
- Analysis of TensorFlow, PyTorch, JAX, scikit-learn, etc.
- Performance benchmarks from community sources
- Ecosystem integration patterns
- When to use each framework
- Learning curve assessments

Key Features

• Quantitative Rankings: Popularity scores based on weighted GitHub metrics
• Growth Analysis: Trends and predictions with probability estimates
• Visual Formatting: Tables, ASCII art, emoji for scannability
• Decision Frameworks: Project-size-specific recommendations
• Migration Guides: Step-by-step strategies with ROI projections
• No Bias: Data-driven analysis, not opinion-based

Skill Components

SKILL.md

Complete workflow documentation with:

• 6-step systematic process
• Quality standards checklist
• Advanced techniques (custom scoring, multi-dimensional analysis)
• Common pitfalls to avoid
• Integration with other skills

scripts/collect_github_data.py

Reusable Python template featuring:

• GitHub API integration
• Popularity scoring algorithm
• Growth rate estimation
• JSON export
• Console rankings
• Easy customization (just edit LIBRARIES dict)

references/report_template.md

Professional report structure with:

• 9 major sections
• Visual formatting guidelines
• Comparative analysis tables
• Actionable recommendations framework
• Data source documentation

Requirements

• Python 3.x with requests library
• GitHub API access (no authentication required for public repos)
• desktop-commander MCP for file operations and process execution
• Web search access (mcp-server-search or similar)

Customization

The skill is highly adaptable:

• Adjust scoring weights: Modify WEIGHTS dict based on ecosystem characteristics
• Custom metrics: Add bundle size, performance benchmarks, TypeScript support
• Ecosystem-specific: Tune MAX_STARS and growth thresholds
• Report sections: Add/remove sections based on user needs

Maintenance

Keep the skill current by:

• Updating max_stars/max_forks for growing ecosystems
• Refining growth rate thresholds as communities mature
• Adjusting weights based on real-world adoption patterns
• Monitoring GitHub API changes

Comparison with Other Skills

vs. web-research-documenter:

• web-research: Qualitative research → documentation
• tech-ecosystem: Quantitative metrics + research → data-driven analysis

vs. generic search:

• Generic: Find information
• tech-ecosystem: Collect data, analyze, score, rank, and recommend

License

See LICENSE.txt for complete terms.

Version

1.0.0 - Initial release (January 2025)