ai-room

Review AI/ML architecture decisions and produce prioritized action plans.

Updated Apr 12, 2026
One-click install
npx skills add https://github.com/KBRglobal/advisiorai --skill ai-room
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-room
Source: https://github.com/KBRglobal/advisiorai/tree/main/skills/ai-room
Command: npx skills add https://github.com/KBRglobal/advisiorai --skill ai-room

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured, multi-expert technical review and verdict for AI/ML decisions so teams get concrete, production-ready guidance rather than vague recommendations.

Core Features & Use Cases

  • Six expert perspectives: Combines opposing viewpoints on model selection, prompt design, agent workflows, RAG/embeddings, fine-tuning, and evaluation frameworks to surface real tradeoffs.
  • Structured deliverables: Produces first-pass analyses, a focused debate, hard preconditions, a confidence score per expert, a targeted risk map, a prioritized week-one plan, and a clear architecture verdict.
  • Use Case: A product team choosing between API-based LLMs vs self-hosted models receives model-specific tradeoffs, deployment constraints, and a 7-day action plan to validate the chosen approach.

Quick Start

Ask the AI board to "review my AI architecture: model choices, RAG design, and deployment constraints" and include architecture diagrams, data properties, and desired latency targets.

Frequently Asked Questions about ai-room

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

FAQPage Schema
How do I evaluate tradeoffs between API-based LLMs and self-hosted models for my architecture?

Evaluating LLM architecture tradeoffs requires analyzing model-specific constraints, deployment limitations, and latency targets. A multi-expert technical review provides concrete model selection verdicts, risk mappings, and a 7-day action plan to validate your chosen approach.

What is the best way to design a RAG and embeddings pipeline for production?

Designing a production RAG pipeline requires applying multi-expert perspectives on embeddings, data quality, and latency constraints. This structured review yields an architecture verdict, prioritized week-one implementation plan, and confidence scores for your vector search setup.

How do I set up an evaluation framework for fine-tuning and prompt engineering?

Setting up an evaluation framework for fine-tuning involves surfacing opposing expert viewpoints on prompt engineering and model evaluation. You receive targeted hard questions, confidence scores per expert, and actionable architecture verdicts referencing deployment constraints.

Can I get a structured technical review for my ML pipeline and agent design decisions?

Structured technical reviews for ML pipeline and agent design decisions combine six expert perspectives to surface real tradeoffs. They produce first-pass analyses, focused debates, preconditions, targeted risk maps, and a clear architecture verdict for your system.

Does multi-expert AI advisory work for choosing model selection strategies under strict latency constraints?

Multi-expert AI advisory handles model selection under latency constraints by mapping risks and scoring expert confidence. It outputs actionable architecture verdicts and a prioritized week-one plan referencing data quality, deployment limits, and model choices.