dependency-evaluator

Evaluating a dependency involves analyzing ten quality signals: maintenance status, security posture, documentation quality, dependency footprint, license compatibility, API stability, community health, production adoption, funding, and ecosystem momentum. This Skill gathers data across npm, PyPI, Cargo, and Go ecosystems automatically and produces a structured recommendation—ADOPT, EVALUATE FURTHER, or AVOID—with evidence-based rationale to support your decision.

The evaluation analyzes maintenance activity, security records, documentation completeness, dependency count and depth, license compatibility with your project, API stability history, community engagement, real-world production usage, active funding, and ecosystem momentum. These signals combine into an actionable recommendation with concrete next steps.

Yes. Beyond evaluating new dependencies before adoption, this Skill supports auditing existing packages in your codebase and comparing alternative packages side by side. Run the evaluator on any package name and ecosystem to generate a comprehensive report with the same ten quality signals.

Run `python3 dependency_evaluator.py <package-name> <ecosystem>` to generate a data.json file with automated data gathering across your chosen package manager (npm, PyPI, Cargo, or Go). Then provide this output to Claude for a summarized assessment and recommendation.

ADOPT indicates the package meets quality thresholds across signals and is safe to integrate. EVALUATE FURTHER means trade-offs exist and warrant deeper investigation before committing. AVOID signals risks—maintenance gaps, security concerns, or licensing conflicts—that outweigh benefits. Each outcome includes concrete next steps.