What are the prerequisites for using this skill?

You need agentic-flow v1.5.11+, AgentDB v1.0.4+ for persistence, and a Node.js 18+ environment.

How does it improve AI performance?

By recording the outcomes of various tasks and analyzing context, it allows agents to recommend and select the most successful strategies for future similar tasks based on historical data.

Does this skill require external storage?

For persistent learning across sessions, it is designed to work with AgentDB (v1.0.4+) to store experiences, patterns, and learned strategies.

Can I use this for transfer learning?

Yes. ReasoningBank allows you to transfer knowledge and patterns from one domain (e.g., JavaScript code review) to a related domain (e.g., TypeScript code review) with defined similarity scores.

What is ReasoningBank Intelligence?

It is a Claude Code skill that provides a framework for AI agents to learn from experience, recognize patterns, and optimize their behavior over time through a structured feedback loop.

ReasoningBank Intelligence

Name: ReasoningBank Intelligence
Author: ricable

byricable

•

Data Science & ML

Implements adaptive learning and meta-cognitive systems to enable AI agents to recognize patterns and optimize strategies through experience.

About

ReasoningBank Intelligence is a specialized Claude Code skill designed for building self-improving AI agents that learn from every interaction. By integrating with agentic-flow and AgentDB, it provides a structured framework for recording task outcomes, matching recurring patterns, and recommending the most effective strategies based on historical performance data. This skill is essential for developers creating autonomous systems that require continuous optimization, meta-learning capabilities, and transfer learning between domains to enhance overall system intelligence and decision-making accuracy.

Key Features

Adaptive pattern recognition for identifying recurring triggers and optimal actions
1 GitHub stars
Strategy optimization to recommend the most successful approaches for specific tasks
Persistent memory integration with AgentDB for long-term knowledge retention
Meta-learning capabilities to improve the agent's internal learning processes
Transfer learning to apply insights from one domain to another related context

Use Cases

Implementing meta-cognitive systems that adapt execution strategies based on real-world constraints
Optimizing complex workflows like code reviews or deployments based on historical metrics
Building self-improving autonomous agents that learn from task successes and failures

About

Key Features

Adaptive pattern recognition for identifying recurring triggers and optimal actions
1 GitHub stars
Strategy optimization to recommend the most successful approaches for specific tasks
Persistent memory integration with AgentDB for long-term knowledge retention
Meta-learning capabilities to improve the agent's internal learning processes
Transfer learning to apply insights from one domain to another related context

Use Cases

Implementing meta-cognitive systems that adapt execution strategies based on real-world constraints
Optimizing complex workflows like code reviews or deployments based on historical metrics
Building self-improving autonomous agents that learn from task successes and failures