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AgentDB Learning Plugins

Create, train, and deploy reinforcement learning plugins with AgentDB's CLI.

Updated Jul 2, 2025
Search Tags:#templates#API#CLI#plugins#training#AgentDB#reinforcement-learning
One-click install
npx skills add https://github.com/dug-21/neural-data-platform --skill agentdb-learning-plugins-dug-21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/dug-21/neural-data-platform/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/dug-21/neural-data-platform --skill agentdb-learning-plugins-dug-21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides access to a suite of reinforcement learning plugins via AgentDB's tooling, enabling developers to create, train, and deploy self-learning AI components that improve through experience.

Core Features & Use Cases

  • Plugin templates: Access to nine RL algorithms including decision-transformer, q-learning, sarsa, actor-critic, and more for rapid plugin development.
  • End-to-end lifecycle: Create plugins, train models, and deploy them for autonomous agents and simulations.
  • Performance & reasoning: WASM-accelerated inference and integrated reasoning to evaluate and improve plugin behavior.

Quick Start

  • CLI Quick Start: npx agentdb@latest create-plugin
  • Create a plugin: npx agentdb@latest create-plugin -t decision-transformer -n dt-agent
  • Preview without creating: npx agentdb@latest create-plugin -t q-learning --dry-run
  • Output directory: npx agentdb@latest create-plugin -t actor-critic -o ./plugins

Frequently Asked Questions about AgentDB Learning Plugins

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

FAQPage Schema
How do I create reinforcement learning plugins for autonomous agents?▼

The Skill provides nine reinforcement learning plugin templates, including decision-transformer, q-learning, sarsa, and actor-critic. These templates enable rapid development of self-learning AI components that improve through experience.

How do I scaffold and train a q-learning model using AgentDB?▼

To scaffold a q-learning model, run npx agentdb@latest create-plugin -t q-learning -n [name]. This automates plugin creation and training orchestration, allowing you to deploy self-learning components for autonomous agents and research workflows.

Can I preview a plugin template before generating the files?▼

Yes, you can preview a plugin template without creating files by using the --dry-run flag. For example, npx agentdb@latest create-plugin -t q-learning --dry-run outputs the scaffold structure for review before committing.

Does this toolkit support actor-critic and decision-transformer algorithms?▼

Yes, the toolkit supports both actor-critic and decision-transformer algorithms among its nine available templates. It orchestrates training and provides reasoning-enabled evaluation to improve plugin behavior for autonomous agents.

How is inference performance handled during plugin evaluation?▼

Plugin evaluation uses WASM-accelerated inference combined with integrated reasoning to evaluate and improve plugin behavior. This ensures deployed learning modules perform efficiently within autonomous agent workflows.

Can I specify a custom output directory for my generated plugins?▼

Yes, you can specify a custom output directory using the -o flag. Running npx agentdb@latest create-plugin -t actor-critic -o ./plugins directs the scaffolded plugin files into your specified path.

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