continuous-learning-v2

To capture session observations and build AI learning instincts, you enable observation hooks in your Claude settings, which reliably record tool usage for background pattern detection and instinct generation.

Confidence scoring for atomic instincts evaluates observed session patterns, assigning reliability scores to evidence-based behaviors before clustering them into more complex skills, commands, or agents.

The best way to evolve learned patterns into specialized debugging skills is using an evolution pipeline that clusters atomic instincts from repeated successful resolutions into reusable commands or agents.

Yes, you need background observation agents and hook integration to detect patterns in Claude Code sessions, as these components continuously monitor interactions to generate personalized AI instincts.

Yes, you can view learned instincts by running the `/instinct-status` command after enabling observation hooks, which displays the atomic instincts generated from your session interactions.

You should not use an instinct-based learning system for AI evolution when your workflow lacks repeated patterns or when continuous background observation agents cannot be integrated via hooks.