What is ReasoningBank Intelligence?

It is a specialized skill for Claude Code that implements an adaptive learning system, allowing AI agents to learn from past experiences and optimize their performance over time.

How does it improve AI agents?

It identifies patterns in task outcomes and suggests the most effective strategies for new tasks based on what has historically produced the best metrics.

Does it require external storage?

Yes, it is optimized to work with AgentDB for persistent storage, ensuring that the patterns and experiences Claude learns are saved across sessions.

What is meta-learning in this context?

Meta-learning allows the system to learn about the learning process itself, such as identifying which conditions make specific execution styles (like parallel vs. sequential) more effective.

Can knowledge be shared between different programming languages?

Yes, the skill supports transfer learning, which allows Claude to apply insights gained in one domain, such as JavaScript code reviews, to similar tasks in another domain like TypeScript.

ReasoningBank Intelligence

Name: ReasoningBank Intelligence
Author: while-basic

bywhile-basic

データサイエンスとML

Implements adaptive learning systems to enable AI agents to recognize patterns, optimize strategies, and improve through experience.

概要

ReasoningBank Intelligence provides a sophisticated framework for building self-improving AI agents within the agentic-flow ecosystem. It allows Claude to record task outcomes, identify successful patterns across different domains, and recommend optimal strategies based on historical data. By integrating with AgentDB for persistence, this skill transforms static LLM workflows into dynamic, meta-cognitive systems that evolve and become more efficient over time, making it ideal for complex, repetitive coding or analysis tasks where performance optimization is critical.

主な機能

0 GitHub stars
Domain-to-domain knowledge transfer for cross-functional efficiency
Adaptive pattern recognition for situational awareness
Automated strategy optimization based on historical task outcomes
Continuous meta-learning to improve decision-making logic
Persistence integration with AgentDB for long-term learning storage

ユースケース

Building self-improving autonomous coding agents
Optimizing CI/CD workflows through historical performance analysis
Implementing adaptive error handling and recovery strategies in complex systems

概要

主な機能

0 GitHub stars
Domain-to-domain knowledge transfer for cross-functional efficiency
Adaptive pattern recognition for situational awareness
Automated strategy optimization based on historical task outcomes
Continuous meta-learning to improve decision-making logic
Persistence integration with AgentDB for long-term learning storage

ユースケース

Building self-improving autonomous coding agents
Optimizing CI/CD workflows through historical performance analysis
Implementing adaptive error handling and recovery strategies in complex systems