continuous-learning-v2

Name: continuous-learning-v2
Availability: InStock
Author: KuaaMU

Observe AI sessions via hooks and create confidence-scored instincts in YAML.

4|Updated Feb 14, 2026

Search Tags:#pattern detection #ai learning #code assistance #instincts #skill evolution #session observation

Ready to run

npx skills add https://github.com/KuaaMU/omnihive --skill continuous-learning-v2-kuaamu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: continuous-learning-v2
Source: https://github.com/KuaaMU/omnihive/tree/main/library/ecc-skills/continuous-learning-v2
Command: npx skills add https://github.com/KuaaMU/omnihive --skill continuous-learning-v2-kuaamu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of capturing your interactions with AI models, identifying recurring patterns, and transforming them into reusable "instincts" that improve AI performance over time.

Core Features & Use Cases

Automatic Observation: Hooks capture every tool use during your sessions for comprehensive data.
Instinct Creation: Identifies user corrections, error resolutions, and repeated workflows to generate atomic, confidence-weighted instincts.
Evolution: Clusters instincts into more complex skills, commands, or agents.
Use Case: As you refine code or debug issues, the system learns your preferred methods and automatically suggests them as instincts, making future AI interactions more efficient and aligned with your style.

Quick Start

Enable observation hooks by adding the provided JSON configuration to your ~/.claude/settings.json file.

Frequently Asked Questions about continuous-learning-v2

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate AI learning from session observations?▼

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.

What is an instinct-based learning system for AI?▼

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.

How to capture AI session data for pattern detection?▼

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.

Can I use hook configurations to build AI instincts?▼

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.

How do generated instincts evolve into commands or agents?▼

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.

What formats are used to store learned AI behaviors and instincts?▼

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.