evolution-engine

Automate skill evolution by analyzing accumulated feedback data to identify patterns, propose rule upgrades, and suggest skill optimizations based on evidence. This ensures platform improvements are data-driven and continuously aligned with user needs.

Rule graduation is the process of proposing that frequently repeated feedback rules be upgraded to official rules. The engine scans accumulated feedback to identify consistently reinforced patterns, promoting them to official platform rules.

Yes, skills with consistently low feedback scores are flagged for optimization. The engine analyzes negative feedback patterns to suggest specific adjustments, ensuring underperforming skills are refined based on actual user data.

New skill proposals are generated by identifying recurring patterns in feedback data that lack existing Skills. The engine scans for unaddressed user needs and leverages data-driven analysis to propose targeted new skill creation.

No external dependencies are required. The engine relies on access to accumulated feedback data and the platform's skill structure to analyze patterns and generate evolution proposals automatically.

Avoid using automated proposals when there is insufficient accumulated feedback data. The engine relies entirely on data-driven analysis, meaning no changes are proposed without concrete evidence from feedback patterns.