skill-optimizer

Skill instructions are optimized automatically through a validation-gated feedback loop based on the SkillOpt methodology. The pipeline iteratively rewrites skill logic using benchmark-driven evaluation and held-out test sets to ensure statistically significant performance gains without overfitting.

Validation-gated skill tuning prevents overfitting by requiring strict adherence to benchmark-disjoint task sets. The optimizer evaluates instruction refinements against held-out test sets, ensuring measured performance gains reflect genuine improvements rather than memorization of benchmark data.

The best way to benchmark agent skill performance is through automated task-based evaluation. The optimizer automatically generates and executes benchmark evaluations against held-out datasets, measuring execution quality to identify and tune underperforming edge cases safely.

Yes, automated skill refinement supports custom AI agent logic. If a custom skill underperforms on specific edge cases, invoke the optimizer to tune the skill's logic against a held-out dataset, ensuring improvements are validated and safe for deployment.

To start self-evolving skill optimization, invoke the optimizer by running the gbrain skillopt command followed by the target skill name and the bootstrap flag. This initiates the automated benchmarking and iterative refinement pipeline to improve execution quality.