adaptive-learner

To extract transferable principles from user feedback, apply a structured discovery and feedback analysis workflow that compares good versus bad outputs to generate actionable, domain-specific learning rules. This methodology enables continuous AI self-improvement across domains.

An AI meta-skill for continuous learning is a methodology that creates domain-specific learning skills by accumulating validated principles from user feedback. It maintains learning history and domain profiles to guide future outputs without replacing existing domain expertise.

To start building a domain-specific learning profile, provide the target domain and initial examples of good versus bad outputs. The system analyzes this feedback to generate and store validated principles for ongoing refinement and future tasks.

Validated learning principles and history are stored in references/principles.md and references/learning-log.md. These files maintain the accumulated domain profiles and structured feedback loops necessary for ongoing AI performance improvement.

This adaptive learning approach does not replace domain expertise. Instead, it provides the structured methodology to extract, store, and apply transferable principles from user feedback to improve AI performance across different domains.