council

Coordinate parallel queries and anonymous peer reviews across multiple AI models.

Updated Feb 23, 2026
One-click install
npx skills add https://github.com/mcevoyinit/agentic-skills --skill council-mcevoyinit
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: council
Source: https://github.com/mcevoyinit/agentic-skills/tree/main/skills/multi-ai/council
Command: npx skills add https://github.com/mcevoyinit/agentic-skills --skill council-mcevoyinit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Convene an AI Council (GPT, Gemini, Grok) for maximum insight. Queries all models in parallel, has them review each other anonymously, then synthesizes a final answer. Inspired by Karpathy's llm-council. Use for major decisions, architecture debates, or when you want the wisdom of multiple AI perspectives.

Core Features & Use Cases

  • Parallel querying of multiple AI models to surface diverse expert opinions.
  • Anonymous peer reviews of responses to reduce attribution bias.
  • Final synthesis with a clear, actionable recommendation and caveats.

Quick Start

Activate the AI Council to gather perspectives from multiple models and synthesize a final, well-supported recommendation.

Frequently Asked Questions about council

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

FAQPage Schema
How do I get multiple AI models to review each other's answers for architecture decisions?

To gather diverse expert perspectives for architecture decisions, query multiple AI models in parallel, enable anonymous peer reviews of their responses, and synthesize a final answer. This cross-model validation surfaces blind spots in major design debates.

What is the best way to validate complex design choices across different AI perspectives?

Validating complex design choices across AI perspectives is best achieved through a three-stage workflow: parallel model inquiries, anonymous peer reviews to eliminate bias, and a synthesized final answer with actionable recommendations and caveats.

Can I use this multi-model approach for high-stakes strategic questions and major decisions?

Yes, you can use this multi-model approach for high-stakes strategic questions. It coordinates parallel inquiries across multiple AI models to gather diverse expert perspectives, applying cross-model validation to major decisions requiring comprehensive analysis.

Does the AI council workflow support anonymous peer reviews to reduce bias?

Yes, the AI council workflow supports anonymous peer reviews. Models review each other's responses anonymously during the peer review stage, which reduces attribution bias before the final synthesis and optional chairman safety checks are applied.

What are the limitations of using cross-model validation for decision support?

Limitations of cross-model validation include reliance on the availability of underlying models for parallel querying and the need for optional chairman synthesis to resolve conflicting viewpoints. It is designed for high-stakes architecture decisions rather than simple queries.

When do I need cross-model validation for a design debate?

You need cross-model validation for a design debate when facing high-stakes architecture decisions or complex strategic questions. It gathers diverse expert perspectives through parallel inquiries to ensure comprehensive analysis and cross-model validation before finalizing.