continuous-learning-v2

Automate learning from AI sessions by enabling observation hooks in your settings to capture interactions. The system detects patterns and user corrections, transforming them into atomic instincts with confidence scoring that evolve into reusable skills or commands.

An instinct-based learning system observes your AI sessions via hooks to create small learned behaviors called instincts. It assigns confidence scores to these instincts and clusters them into complex skills, commands, or agents based on your repeated workflows and error resolutions.

Capture user corrections and repeated workflows by enabling observation hooks in your ~/.claude/settings.json. The system automatically detects these session patterns, generates atomic instincts, and clusters them to suggest new automated commands or agents.

Yes, you can use YAML for instinct definition alongside Python scripts for CLI management. This combination allows the system to capture session data, assign confidence scores, and evolve instincts into functional skills, commands, or agents.

Pattern detection for personalized AI capabilities works by observing your session data to identify tool usage, error resolutions, and repeated workflows. It captures these patterns as instincts with confidence scoring, then clusters them to build automated behaviors.

Yes, you need to enable observation hooks in your ~/.claude/settings.json to start learning. This activates the system to automatically capture session data, detect patterns, and generate instincts with confidence scoring for evolving into skills.