Does this skill require a database for persistence?

Yes, for data persistence across sessions, it is designed to work with AgentDB v1.0.4+ which provides storage and vector search capabilities for learned patterns.

What are the prerequisites for using this skill?

You need agentic-flow v1.5.11+, AgentDB v1.0.4+ for storage, and Node.js 18 or higher.

What is ReasoningBank Intelligence?

It is a Claude Code skill that implements adaptive learning, allowing AI agents to recognize patterns and improve their performance over time by recording and analyzing experiences.

Can it be used for transfer learning between domains?

Yes, it includes a transferKnowledge feature that allows the system to apply logic and experience from one domain (e.g., JavaScript) to another similar domain (e.g., TypeScript).

How does the strategy optimization work?

The skill compares different approaches for a task and uses historical outcome data to recommend the strategy with the highest success rate and best performance metrics.

ReasoningBank Intelligence

Name: ReasoningBank Intelligence
Author: ellisapotheosis

byellisapotheosis

データサイエンスとML

Equips AI agents with adaptive learning and pattern recognition capabilities to optimize strategies based on historical performance.

概要

ReasoningBank Intelligence is a specialized meta-cognitive skill designed for the agentic-flow ecosystem that enables Claude to build self-improving agents. By recording task outcomes, analyzing context-specific patterns, and persisting data via AgentDB, it allows for continuous improvement in strategy selection and execution. This skill is ideal for developers building autonomous systems that need to learn from experience, optimize complex workflows, or implement transfer learning between different domains to increase efficiency over time.

主な機能

Adaptive pattern recognition for intelligent situational awareness
Meta-learning capabilities to improve the learning process itself
Continuous learning loops that update models from high-confidence outcomes
Dynamic strategy optimization based on historical success metrics
0 GitHub stars
Seamless AgentDB integration for persistent, vector-searchable experience storage

ユースケース

Optimizing software development workflows like code reviews and bug fixing through historical analysis
Developing self-improving autonomous agents that refine their behavior based on past outcomes
Implementing transfer learning to apply logic from one programming language or domain to another

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ellisapotheosis/project-nyra reasoningbank-intelligence

For use in Claude.ai and ChatGPT

Download Skill

GitHub

概要