TeamAgent

Name: TeamAgent
Availability: InStock
Author: libz-renlab-ai

Extract user corrections into reusable rules stored as JSON for AI conversations.

13|3|Updated Apr 22, 2026

Search Tags:#AI learning #rule extraction #conversation memory #rule distillation #dialogue enhancement

Ready to run

npx skills add https://github.com/libz-renlab-ai/TeamBrain --skill teamagent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: TeamAgent
Source: https://github.com/libz-renlab-ai/TeamBrain/tree/main/.agents/skills/hyperframes
Command: npx skills add https://github.com/libz-renlab-ai/TeamBrain --skill teamagent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables AI language models to learn from user corrections in conversations, capturing reusable rules to improve future interactions.

Core Features & Use Cases

Rule Extraction: Automatically distills user corrections during dialogue into reusable rules for AI behavior.
Memory Enhancement: Provides a method for AI models to simulate memory across sessions by storing extracted rules.
Use Case: A user corrects the AI's coding style; the system extracts the style guidelines and applies them in subsequent sessions to maintain consistency.

Quick Start

Use the TeamAgent Skill to extract and store conversational rules based on recent corrections during dialogue, improving AI consistency over multiple sessions.

Frequently Asked Questions about TeamAgent

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

FAQPage Schema

How do I make an AI remember user corrections across different chat sessions?▼

To make an AI remember user corrections across sessions, you can use rule extraction to distill dialogue corrections into reusable JSON rules. This allows the model to simulate long-term memory and apply learned preferences in future interactions.

How does rule extraction from AI conversations actually work?▼

Rule extraction works by automatically distilling user corrections during dialogue into reusable behavioral rules. These extracted rules are stored in a JSON configuration, allowing the AI to maintain consistency and adapt to user preferences over time.

Do I need Python to configure conversation memory and rule distillation?▼

Yes, implementing conversation memory and rule distillation requires Python scripts alongside JSON configuration files for rule storage. This setup enables the system to extract, save, and apply user-corrected rules during AI interactions.

Can I use this rule extraction approach for a rule-based tutoring system?▼

Yes, this rule extraction approach is suitable for rule-based tutoring systems, chatbots, and AI assistants. It enables these systems to capture reusable rules from user corrections, ensuring adaptive behavior and consistent dialogue enhancement.

What is the best way to maintain consistent AI coding style after user feedback?▼

The best way to maintain consistent AI coding style is to extract style guidelines from user feedback during dialogue into reusable rules. By storing these rules in JSON, the AI can automatically apply the corrected style in subsequent sessions.

Why does my AI assistant fail to apply previously corrected rules in new sessions?▼

An AI assistant fails to apply corrected rules across sessions because it lacks persistent memory. By using Python scripts to extract and store these corrections as JSON rules, the model can simulate memory and retrieve them for future dialogue.