Do I need to install any external software?

Yes, this skill requires the 'agent-council' CLI to be installed globally via npm (npm install -g agent-council) to function properly.

How do I trigger a council decision?

Simply run the command 'agent-council' followed by your specific question or dilemma within your terminal to initiate the multi-model pipeline.

Which AI models are included in the council?

The council leverages the combined intelligence of Anthropic's Claude, OpenAI's Codex, and Google's Gemini to provide diverse technical perspectives.

How does the three-stage voting pipeline work?

First, three models answer the question independently. Second, they rank each other's answers based on quality. Finally, an AI chairman synthesizes the results into a single, cohesive recommendation.

When should I use this skill instead of a standard Claude query?

Use this skill for complex, subjective, or high-stakes decisions where single-model bias might occur, such as architectural designs or stack choices.

Multi-Model AI Council

Name: Multi-Model AI Council
Author: mylukin

bymylukin

•

Data Science & ML

Generates consensus-driven technical decisions by synthesizing insights from Claude, Codex, and Gemini through a three-stage voting pipeline.

About

The Council Decision skill empowers developers to resolve complex technical dilemmas by leveraging an ensemble of leading AI models. By running the agent-council CLI, it facilitates a rigorous process where Claude, Codex, and Gemini provide independent solutions, peer-rank each other's work, and have a final recommendation synthesized by a chairman. This approach significantly minimizes single-model bias and provides diverse perspectives on architectural design, technology stack choices, and implementation patterns, ensuring higher-quality decision-making for critical system structures.

Key Features

Multi-model ensemble utilizing Claude, Codex, and Gemini
Direct CLI integration for seamless workflow inclusion
Elimination of single-model bias for high-stakes technical decisions
20 GitHub stars
Automated synthesis of final recommendations by an AI chairman
Rigorous three-stage voting and peer-ranking pipeline

Use Cases

Evaluating architectural patterns like WebSockets vs. SSE for real-time systems
Choosing between SQL and NoSQL databases based on specific project requirements
Determining the most efficient implementation strategy for complex API caching

About

Key Features

Multi-model ensemble utilizing Claude, Codex, and Gemini
Direct CLI integration for seamless workflow inclusion
Elimination of single-model bias for high-stakes technical decisions
20 GitHub stars
Automated synthesis of final recommendations by an AI chairman
Rigorous three-stage voting and peer-ranking pipeline

Use Cases

Evaluating architectural patterns like WebSockets vs. SSE for real-time systems
Choosing between SQL and NoSQL databases based on specific project requirements
Determining the most efficient implementation strategy for complex API caching