kaia

Guide architecture reviews and trade-off analyses for the Marcus multi-agent coordination platform.

13|11|Updated Jun 16, 2025
One-click install
npx skills add https://github.com/lwgray/marcus --skill kaia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kaia
Source: https://github.com/lwgray/marcus/tree/main/skills/kaia
Command: npx skills add https://github.com/lwgray/marcus --skill kaia

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Dr. Kaia Chen provides architecture guidance for Marcus's board-mediated multi-agent coordination platform, helping teams align on design decisions and production readiness.

Core Features & Use Cases

  • Modes of Operation: Quick Advice, Architecture Review, Research, Reflection, Mentorship.
  • Guides architecture decisions, performs reviews against codebase, and mentors team members through design choices.
  • Use Cases: architecture design sessions, trade-off analyses, and production-readiness evaluations.

Quick Start

Ask Kaia for architectural guidance on Marcus and receive a structured analysis with trade-offs and next steps.

Frequently Asked Questions about kaia

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

FAQPage Schema
How do I conduct an architecture review for a multi-agent coordination platform?▼

An architecture review for a multi-agent coordination platform evaluates design decisions and production readiness by analyzing trade-offs against the actual codebase and MCP tooling. It ensures observability, reliability, and safety across the system.

What is board-mediated multi-agent coordination and when do I need it?▼

Board-mediated multi-agent coordination is an architectural pattern where agents interact through a centralized board mechanism. You need it when scaling multi-agent systems requires structured coordination, production-grade reliability, and strict safety controls.

How do I analyze trade-offs for production-readiness decisions in multi-agent systems?▼

Analyzing trade-offs for production-readiness in multi-agent systems involves grounding design decisions in the actual codebase and MCP tooling. This process evaluates observability, reliability, and safety to determine if the architecture is deployment-ready.

Can I get mentorship on architectural design choices for an MCP tooling environment?▼

Yes, architectural mentorship for an MCP tooling environment guides team members through complex design choices. It focuses on reviewing design decisions, performing trade-off analyses, and logging decisions to align on production readiness.

What are the limitations of using board-mediated coordination for multi-agent systems?▼

Limitations of board-mediated coordination include potential bottlenecks in the central board mechanism and the need for rigorous observability. Production readiness requires careful safety and reliability trade-off analysis to mitigate single-point-of-failure risks.