What is ReasoningBank Intelligence?

It is an adaptive learning system that enables AI agents to learn from experience, recognize patterns, and optimize their decision-making over time.

Can I use this for non-coding tasks?

Absolutely. While it excels in development workflows, its pattern recognition and meta-learning capabilities can be applied to any structured task or workflow automation.

Does this skill require a database?

Yes, for persistent learning across sessions, it is designed to work with AgentDB v1.0.4 or higher to store experiences and patterns.

How does the agent know which strategy to use?

The skill uses the recommendStrategy function to compare historical outcomes and select the approach with the highest success rate and confidence score for the given context.

ReasoningBank Intelligence

Name: ReasoningBank Intelligence
Author: whrit

bywhrit

データサイエンスとML

Empowers AI agents with adaptive learning and pattern recognition to optimize workflows and decision-making strategies through experience.

概要

ReasoningBank Intelligence is a specialized skill designed for building self-improving agentic systems within the Claude Code environment. By implementing a meta-cognitive layer, it allows agents to record task outcomes, recognize recurring patterns, and recommend optimal strategies based on historical performance data. It integrates seamlessly with AgentDB for persistent memory, enabling transfer learning across different domains and continuous improvement of autonomous workflows without manual intervention.

主な機能

0 GitHub stars
Strategy optimization based on success metrics and confidence scores
Meta-learning capabilities to improve the learning process itself
Transfer learning to apply knowledge across diverse task domains
Adaptive learning from task outcomes and experiences
Pattern recognition for automated trigger-action matching

ユースケース

Building autonomous coding agents that learn from past debugging sessions
Optimizing complex CI/CD workflows based on historical success rates
Creating self-improving research agents that refine their search strategies

概要

主な機能

0 GitHub stars
Strategy optimization based on success metrics and confidence scores
Meta-learning capabilities to improve the learning process itself
Transfer learning to apply knowledge across diverse task domains
Adaptive learning from task outcomes and experiences
Pattern recognition for automated trigger-action matching

ユースケース

Building autonomous coding agents that learn from past debugging sessions
Optimizing complex CI/CD workflows based on historical success rates
Creating self-improving research agents that refine their search strategies