continuous-learning-v2

Capture and codify coding patterns from Claude Code session activity.

Updated May 9, 2026
One-click install
npx skills add https://github.com/RambleRainbow/jd --skill continuous-learning-v2-ramblerainbow
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: continuous-learning-v2
Source: https://github.com/RambleRainbow/jd/tree/main/.claude/skills/continuous-learning-v2
Command: npx skills add https://github.com/RambleRainbow/jd --skill continuous-learning-v2-ramblerainbow

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, git, claude-cli, and includes scripts (resource) components.

What problem does it solve?

Manually documenting coding patterns, project conventions, and workflow best practices across multiple projects is time-consuming and error-prone. This Skill automatically learns from your Claude Code sessions to build a reusable library of behaviors tailored to each of your projects, eliminating the need for manual documentation of repetitive patterns.

Core Features & Use Cases

  • Automatic Session Observation: Hooks capture 100% of Claude Code tool use to detect patterns like user corrections, error resolutions, and repeated workflows without any manual logging.
  • Project-Scoped Instincts: Learned behaviors are isolated per project by default, so React patterns stay in your React projects and Python conventions stay in your Python projects, eliminating cross-project contamination.
  • Instinct Evolution & Promotion: Atomic learned instincts can be clustered into full skills, commands, or agents, and high-confidence universal patterns (like input validation) can be automatically promoted to global scope for use across all your projects.
  • Use Case: If you work on both a React frontend and a Python backend, the Skill will learn React-specific hooks usage for your frontend project and Python type hint conventions for your backend, while sharing universal practices like sanitizing user input across both.

Quick Start

Enable the observation hooks in your Claude Code settings, then work normally in your git-tracked projects; the system will automatically detect patterns from your sessions and build a library of project-specific and global instincts you can review, evolve, and promote as needed.

Frequently Asked Questions about continuous-learning-v2

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

FAQPage Schema
How do I automatically capture coding patterns from Claude Code sessions?

You can capture coding patterns from Claude Code sessions by enabling observation hooks that record 100% of tool use, automatically detecting repetitive workflows and error resolutions without manual logging.

How does project-scoped instinct isolation work for different codebases?

Project-scoped instinct isolation works by storing learned behaviors per git-tracked project by default, ensuring React patterns stay in React projects and Python conventions stay in Python projects, eliminating cross-project contamination.

Do I need git and python3 to use continuous learning hooks with Claude CLI?

Yes, you need git, python3, and the claude-cli installed to use continuous learning hooks, as the system relies on git-tracked projects for scoping and Python scripts for processing session observation data.

Can learned coding instincts be promoted across multiple projects?

Yes, learned coding instincts can be promoted across multiple projects. High-confidence universal patterns like input validation are automatically promoted to global scope for use across all your active codebases.

What is the best way to turn repeated workflow patterns into reusable skills?

The best way to turn repeated workflow patterns into reusable skills is to cluster atomic learned instincts into full skills, commands, or agents through instinct evolution, building a library of project-specific and global behaviors.

Why does manual documentation of project conventions fail across multiple repositories?

Manual documentation of project conventions fails across multiple repositories because it is time-consuming and error-prone, struggling to keep pace with evolving best practices and isolated requirements across different tech stacks.