skill-logger

To track and score skill usage quality, you need structured logging of invocation data, execution details, and quality signals. This captures output effectiveness and user satisfaction signals to generate comprehensive analytics for performance monitoring.

Skill performance analytics analyzes skill effectiveness through metrics and feedback loops. You need continuous improvement frameworks when you want to identify areas for improvement and enable data-driven enhancements based on invocation logging and quality scoring.

Log invocation data by capturing inputs, outputs, execution time, and tool calls. Quality scoring uses a multi-dimensional system based on completion, efficiency, output quality, and user satisfaction to support automated alerting when performance drops.

Yes, logging and metrics can monitor multiple skills by tracking invocation data and scoring output quality. You can receive automated alerts when the average quality score drops below a defined threshold, enabling continuous improvement across skills.

The best way to analyze skill performance is through multi-dimensional quality scoring combined with feedback loops. This approach evaluates completion, efficiency, and user satisfaction to identify specific improvement opportunities within your skills.

Yes, structured logging is required to measure output effectiveness and user satisfaction signals. It captures invocation data, execution details, and quality signals needed for comprehensive analytics and accurate performance scoring.