saga-skill-evolution

Automated skill evolution in the SAGA framework works by executing a weekly Goal-GAN cycle to assess skill fitness, mutate underperforming candidates, and retire ineffective skills based on success rate, revenue, efficiency, and user satisfaction.

To automate AI skill optimization, you initiate a skill evolution cycle that evaluates active skills and applies a genetic algorithm to mutate underperforming ones. This performance tuning process automatically adjusts parameters and eliminates obsolete skills.

Yes, you can manually trigger a skill evaluation cycle for AI optimization. The system supports initiating a manual skill optimization cycle outside of the standard weekly schedule to assess fitness and apply mutations immediately.

Yes, you need SAGA core agents to run the Goal-GAN evolution cycle. The automated evolution and optimization of AI skills requires integration with SAGA's core agents and performance metrics to assess fitness and generate reports.

Metrics used to determine AI skill fitness during automated evolution include success rate, revenue, efficiency, and user satisfaction. The Goal-GAN cycle evaluates these performance metrics weekly to decide whether to mutate or retire skills.

You track AI skill retirement and mutations by reviewing the comprehensive evolution reports generated after a Goal-GAN cycle. These detailed weekly reports provide system administrators with full oversight of skill fitness and optimization outcomes.