continuous-learning-v2

To automate AI workflow learning, this Skill uses hooks to observe session activity and captures user patterns as atomic instincts with confidence scoring. It transforms repeated workflows into reusable instincts that can evolve into commands or agents.

Instinct-based pattern recognition works by monitoring session activity via hooks to create atomic learned behaviors. These observed workflows are assigned confidence scores and clustered to evolve into more complex AI components like skills or agents.

Yes, you can isolate learned AI patterns using project-scoped instincts. This feature prevents cross-project contamination by isolating detected workflow patterns to specific projects, keeping learned behaviors strictly context-aware.

Yes, you need Python3 and Git to use this AI learning system. Python scripts handle pattern detection and instinct evolution, while Git workflows provide the observable session activity required for the hook-based learning mechanism.

The best way to evolve learned session patterns is through the evolution pipeline, which clusters atomic instincts and promotes them into reusable AI commands or skills. You can eventually promote these complex components to a global scope.

To enable observation hooks for AI session learning, you configure the hook settings in your `~/.claude/settings.json` file. This setup allows the system to begin observing sessions and capturing workflow patterns for instinct generation.