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ReasoningBank Intelligence

Implement adaptive learning for AI agents with AgentDB persistence and pattern matching.

Updated Mar 5, 2026
Search Tags:#optimization#ai#learning#meta-cognition#pattern recognition#agentic
One-click install
npx skills add https://github.com/bjorkgard/convention-hosts --skill reasoningbank-intelligence-bjorkgard
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank Intelligence
Source: https://github.com/bjorkgard/convention-hosts/tree/main/.agents/skills/reasoningbank-intelligence
Command: npx skills add https://github.com/bjorkgard/convention-hosts --skill reasoningbank-intelligence-bjorkgard

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables AI agents to learn from their experiences, recognize patterns, and continuously optimize their strategies, leading to self-improvement and meta-cognitive capabilities.

Core Features & Use Cases

  • Pattern Recognition: Identifies recurring trends and correlations in data.
  • Strategy Optimization: Recommends the most effective approaches for given tasks.
  • Continuous Learning: Allows agents to adapt and improve over time.
  • Use Case: An AI agent responsible for code reviews can use this Skill to learn which analysis techniques are most effective for different programming languages and code complexities, thereby improving its bug detection rate and efficiency.

Quick Start

Use the ReasoningBank Intelligence skill to record a task outcome for 'code_review' with approach 'static_analysis_first' and success true.

Frequently Asked Questions about ReasoningBank Intelligence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement adaptive learning for AI agents to improve their task performance?▼

Adaptive learning for AI agents is implemented by recording task outcomes and adjusting strategies for continuous improvement. This Skill uses meta-cognitive systems and pattern recognition to help agents learn from experiences and optimize their approaches over time.

What is meta-cognition in AI and how does it help with strategy optimization?▼

Meta-cognition in AI enables agents to analyze their own reasoning processes and recognize recurring patterns. By evaluating successful approaches, the system recommends optimized strategies for specific tasks and facilitates continuous self-improvement.

Can I use pattern recognition to help an AI agent optimize code review tasks?▼

Yes, pattern recognition can optimize code review tasks by identifying recurring trends in code complexities. An AI agent records task outcomes to learn which analysis techniques are most effective for different programming languages, improving bug detection rates and efficiency.

How do I record a task outcome so my AI agent can learn from the result?▼

To record a task outcome, specify the task type, the approach used, and the success status. For example, record a task outcome for 'code_review' with approach 'static_analysis_first' and success set to true to trigger the learning mechanism.

Does this adaptive learning system require a database for persistence?▼

Yes, adaptive learning requires a database for persistence. This Skill utilizes AgentDB to store task outcomes, which enables the agentic-flow to adjust learning rates, match patterns, and maintain continuous improvement over time.

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