multi-agent-orchestrator

This skill enables one-click generation of multiple AI agents based on a user prompt, outputs their organizational structure, and visualizes their collaborat...

brandon-zhanghaodong

@brandon-zhanghaodong

What This Skill Does

Generates a team of specialized AI agents from a natural language prompt, outputs their organizational structure, and visualizes collaboration workflows as swimlane diagrams and org charts.

Replaces manually designing multi-agent systems and their coordination diagrams by automating agent generation and visualization from a single prompt.

When to Use It

  • Rapidly prototype a multi-agent team for a software development project
  • Visualize agent roles and collaboration patterns for a marketing campaign
  • Generate an organizational chart of AI agents for a new product launch
  • Design a multi-agent workflow for market research on an AI product
  • Create swimlane diagrams to illustrate agent interactions in a business process

Install

$ openclaw skills install @brandon-zhanghaodong/multi-agent-orchestrator

Multi-Agent Collaboration Skill

Overview

This skill streamlines the creation and management of multi-agent AI systems. It allows users to define a task with a natural language prompt, and the skill automatically generates a team of specialized agents, visualizes their organizational structure, and illustrates their collaborative workflows using industry-standard diagrams.

Usage

To use this skill, invoke the orchestrate_and_visualize.py script with a natural language prompt describing the multi-agent system you wish to generate and visualize.

orchestrate_and_visualize.py

This script takes a user prompt, generates a team of collaborative agents, and then produces visual representations of their organizational structure and collaboration patterns.

Input Parameters:

  • prompt (string, required): A natural language description of the task or system for which you want to generate agents (e.g., "Create a software development team for an e-commerce platform.", "Conduct market research for a new AI product.").
  • output_dir (string, optional): The directory where the generated Mermaid files and PNG images will be saved. Defaults to the current directory.

Output:

The script will output the following files to the specified output_dir:

  • org_chart.mmd: Mermaid code for the organizational chart.
  • org_chart.png: PNG image of the organizational chart.
  • swimlane.mmd: Mermaid code for the swimlane diagram.
  • swimlane.png: PNG image of the swimlane diagram.

Example Usage:

python /home/ubuntu/skills/multi-agent-orchestrator/scripts/orchestrate_and_visualize.py \
  --prompt "Design a marketing campaign for a new sustainable energy product." \
  --output_dir "/home/ubuntu/marketing_agents"

This will generate the agent team, their organizational chart, and a swimlane diagram showing their collaboration, saving all outputs to /home/ubuntu/marketing_agents/.

Resources

This skill includes the following resources:

scripts/

  • generate_agents.py: Generates a set of agents and their initial configurations based on a given prompt.
  • visualize_collaboration.py: Generates Mermaid organizational charts and swimlane diagrams from agent data.
  • orchestrate_and_visualize.py: Orchestrates the agent generation and visualization process, rendering Mermaid diagrams to PNG images.

references/

  • api_reference.md: (Placeholder) This file can be used for detailed API documentation or specific guidelines for agent interaction protocols.

templates/

  • example_template.txt: (Placeholder) This file can be used for boilerplate code or standard output formats for agents.

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