Does this skill require a database to work?

While it can run in memory, using AgentDB (v1.0.4+) is recommended for persisting learned patterns and experiences across different sessions.

Is it compatible with existing agentic frameworks?

It is specifically designed to work with agentic-flow v1.5.11+ and the broader SPARC framework ecosystem.

What is the primary benefit of ReasoningBank Intelligence?

It allows AI agents to learn from their successes and failures, meaning they get faster and more accurate at specific tasks the more they perform them.

How does it handle strategy optimization?

It compares multiple approaches for a task—such as different debugging methods—and uses historical performance metrics to recommend the most successful one.

Can I use this for transfer learning?

Yes, the skill includes a transferKnowledge feature that allows an agent to apply patterns learned in one domain (like JavaScript) to a similar domain (like TypeScript).

ReasoningBank Intelligence

Name: ReasoningBank Intelligence
Author: ricable

byricable

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数据科学与机器学习

Empowers AI agents with adaptive learning and meta-cognitive capabilities to optimize strategies based on past experiences.

关于

ReasoningBank Intelligence is a specialized skill designed for building self-improving AI agents that learn from every interaction. By integrating with agentic-flow and AgentDB, it enables agents to recognize recurring patterns, compare task execution strategies, and refine their behavior over time. This skill is essential for developers creating sophisticated agentic systems that require meta-learning, transfer learning, and persistent memory to handle complex, evolving workflows with increasing efficiency.

主要功能

Strategy Optimization to compare and select the most effective approach for specific contexts
Transfer Learning capabilities to apply knowledge from one domain to another (e.g., JS to TS)
Native AgentDB Integration for persistent storage and semantic vector-based search
Adaptive Pattern Recognition for identifying recurring task triggers and optimal actions
Continuous Learning system that automatically updates models from high-confidence outcomes
2 GitHub stars

使用场景

Developing self-improving agents for automated code reviews and bug discovery
Optimizing complex data processing pipelines through adaptive execution strategies
Creating meta-cognitive systems that audit and improve their own learning processes

关于

主要功能

Strategy Optimization to compare and select the most effective approach for specific contexts
Transfer Learning capabilities to apply knowledge from one domain to another (e.g., JS to TS)
Native AgentDB Integration for persistent storage and semantic vector-based search
Adaptive Pattern Recognition for identifying recurring task triggers and optimal actions
Continuous Learning system that automatically updates models from high-confidence outcomes
2 GitHub stars

使用场景

Developing self-improving agents for automated code reviews and bug discovery
Optimizing complex data processing pipelines through adaptive execution strategies
Creating meta-cognitive systems that audit and improve their own learning processes