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xleliberty
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agent-designer

Designs scalable multi-agent system architectures and generates tool schemas.

Updated Nov 3, 2016
Search Tags:#architecture#system design#ai agents#orchestration#multi-agent systems#performance analysis
One-click install
npx skills add https://github.com/xleliberty/mydotfiles --skill agent-designer-xleliberty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-designer
Source: https://github.com/xleliberty/mydotfiles/tree/main/.config/.claude/plugins/cache/claude-code-skills/engineering-advanced-skills/2.1.2/agent-designer
Command: npx skills add https://github.com/xleliberty/mydotfiles --skill agent-designer-xleliberty

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Designing complex multi-agent systems is prone to architectural bottlenecks, inefficient communication, and poor performance, which this skill resolves through structured design patterns and rigorous evaluation.

Core Features & Use Cases

  • Architecture Planning: Automatically selects and designs optimal agent patterns like Supervisor, Swarm, or Pipeline based on your specific system requirements.
  • Tool Schema Generation: Creates production-ready tool schemas compatible with OpenAI and Anthropic formats, including built-in validation and error handling.
  • Performance Evaluation: Analyzes execution logs to identify latency bottlenecks, cost inefficiencies, and agent failure patterns.

Quick Start

Use the agent-designer skill to generate a multi-agent architecture based on the requirements defined in assets/sample_system_requirements.json.

Frequently Asked Questions about agent-designer

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

FAQPage Schema
How do I design a multi-agent system architecture for complex tasks?▼

Design multi-agent system architectures by selecting optimal patterns like Supervisor, Swarm, or Pipeline based on your specific task complexity and performance constraints. This approach provides standardized frameworks for agent role definition and communication topology.

What is the best way to evaluate multi-agent system performance and identify bottlenecks?▼

Evaluate multi-agent system performance by analyzing execution logs to identify latency bottlenecks, cost inefficiencies, and agent failure patterns. This process generates automated reports to help optimize orchestration and overall system efficiency.

Can I generate tool schemas compatible with OpenAI and Anthropic formats for AI agents?▼

Generate production-ready tool schemas compatible with OpenAI and Anthropic formats directly from your architecture requirements. The generated schemas include built-in validation and error handling for robust agent orchestration.

When should I use Swarm versus Supervisor patterns for multi-agent orchestration?▼

Select Swarm, Supervisor, or Pipeline orchestration patterns by matching the architecture to your specific task complexity and performance constraints. The automated selection process evaluates your requirements to determine the optimal communication topology.

How do I implement guardrails and role definitions for a multi-agent system?▼

Implement system-wide guardrails and agent role definitions using standardized architectural frameworks. These frameworks ensure structured communication topology and prevent inefficient interactions across your multi-agent orchestration system.

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