continuous-learning-v2

Instinct-based learning works by observing user sessions in Microclaw to create atomic instincts with confidence scoring. These instincts capture your repeated corrections and preferred workflows, evolving into reusable skills that can be automatically applied.

To start evolving Microclaw skills, you provide the continuous learning system with a sample user session. The system analyzes the session data to identify behavioral patterns, generating atomic instincts that progressively develop into fully automated skills.

Confidence scoring in skill evolution evaluates the reliability of observed patterns from user sessions. It assigns a score to atomic instincts, ensuring that only highly validated and consistent behaviors evolve into permanent, reusable Microclaw skills.

Yes, implementing continuous learning requires the underlying Microclaw architecture. Additionally, you must provide the system with direct access to your user sessions and manual corrections so it can accurately observe patterns and generate validated instincts.

An instinct-based learning approach eliminates the manual configuration of repetitive tasks. By automatically observing sessions and scoring confidence, it dynamically evolves workflows into skills, adapting to your specific code correction habits without requiring manual rule setup.