continuous-learning-v2

Automate AI learning from session observations by enabling hooks that capture tool usage, detecting patterns like user corrections, and storing these behaviors as confidence-weighted instincts in YAML format. The system analyzes session activity to generate atomic instincts that evolve into more complex skills, commands, or agents.

An instinct-based learning system is a mechanism that observes AI sessions via hooks, identifies recurring workflows and error resolutions, and creates atomic, confidence-weighted instincts. These instincts evolve into reusable skills, commands, or agents to align future AI interactions with your preferred methods.

Capture AI session data for pattern detection by adding the provided JSON configuration to your `~/.claude/settings.json` file. This enables observation hooks that automatically record every tool use during your sessions for comprehensive activity analysis and instinct generation.

Yes, you can use hook configurations to build AI instincts by integrating the provided JSON settings into your `~/.claude/settings.json` file. The hooks automatically observe session activity, detect user corrections, and generate atomic instincts with confidence scoring to evolve into complex skills.

Generated instincts evolve into commands or agents through a clustering process that groups atomic, confidence-weighted instincts into more complex structures. The system analyzes session patterns and repeatedly observed behaviors to transform learned instincts into executable skills, commands, or autonomous agents.

Learned AI behaviors and instincts are stored in YAML format. The system saves confidence-weighted atomic instincts and clustered skill evolutions as YAML files to document session observations and pattern detection results for future AI interactions.