tech-ecosystem-analyzer

To analyze a technology ecosystem, this skill fetches GitHub repository metrics like stars, forks, and watchers, computes weighted popularity scores from 0-100, and generates a Markdown report to guide project selection.

Comparing multiple libraries for migration planning involves running a Python data collection script to evaluate 3+ options across GitHub activity metrics, producing a raw JSON dataset and structured ranking recommendations.

No, you cannot use this ecosystem analyzer without the Python environment, because the data collection pipeline explicitly requires the `requests` dependency to fetch GitHub metrics and compute popularity scores.

The ecosystem ranking includes both quantitative GitHub metrics and qualitative web research, combining computed popularity scores with broader insights to deliver structured, data-driven recommendations.

You generate a reproducible JSON dataset by running the data collection pipeline on your chosen ecosystem, which outputs raw repository metrics and computed scores to a specified directory for reproducibility.

A limitation of using GitHub metrics for technology trend analysis is that popularity scores rely heavily on repository activity data, which may not fully reflect production readiness or qualitative ecosystem health.