skill-gym

Analyze skill usage data from state.db and Drewgent vault metadata to identify unused skills for deletion. The system evaluates long-term inactivity and file size to pinpoint candidates for removal, optimizing storage and maintaining agent efficiency.

Skill health monitoring tracks skill utilization patterns to identify unused or underutilized skills in a growing project. It analyzes historical usage data from state.db to recommend activation or deletion, ensuring your Drewgent agent remains relevant and efficient.

Generate a weekly report on skill usage analysis by running the skill-gym command. This utilizes Python scripting to process state.db and Drewgent vault metadata, producing insights into underutilized skills and recommending unused skills for activation.

Yes, skill usage analysis requires access to state.db and Drewgent skill metadata. These data sources provide the historical usage and file size information necessary to evaluate skill health and recommend activation or deletion candidates.

The best way to recommend unused skills for activation is to analyze historical usage, relevance, and surprise factors from state.db. This recommendation system identifies underutilized skills that may still provide value to your project workflow.

Your skill management system suggests deleting long-inactive skills to maintain skill health and optimize storage usage. By analyzing historical inactivity and file size, it identifies candidates that no longer contribute value to your project.