When should I use the ask-council skill?

Use it when you face high-complexity problems, need a second (and third) opinion on a refactor, or want to verify the best approach for a new feature.

Does it run the models in parallel?

Yes, the skill handles parallel execution and polling across different providers, delivering all results back to your Claude session efficiently.

What models does the ask-council skill use?

By default, it queries gpt-5.2-pro, gemini-3-pro-preview, and claude-opus-4-5-20251101 simultaneously to provide a broad range of perspectives.

How does an ensemble consultation help with coding?

It provides a 'council' of perspectives, helping you catch edge cases, security vulnerabilities, or architectural flaws that a single model might overlook.

Council Consultation Ensemble

Name: Council Consultation Ensemble
Author: doodledood

bydoodledood

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Ciencia de Datos y ML

Leverages a multi-model ensemble to provide diverse, parallel insights from GPT, Gemini, and Claude for complex problem-solving.

Acerca de

The ask-council skill empowers Claude Code users to seek consensus or diverse perspectives by querying three major AI models simultaneously. By running gpt-5.2-pro, gemini-3-pro-preview, and claude-opus-4-5-20251101 in parallel, it eliminates single-model bias and provides a comprehensive 'council' of opinions. This is particularly useful for high-stakes architectural decisions, debugging elusive edge cases, or validating complex logic where a single viewpoint might miss critical nuances during the development process.

Características Principales

Ensemble-based decision making to reduce model bias
Parallel execution across three top-tier LLMs
Integration with GPT-5.2, Gemini 3 Pro, and Claude Opus
Customizable model selection for specialized domain queries
Automated polling and result relay for seamless workflows
8 GitHub stars

Casos de Uso

Brainstorming creative solutions for unique technical challenges with diverse AI viewpoints
Validating critical system architecture designs through multi-model consensus
Resolving complex logic disagreements during code review or debugging

Acerca de

Características Principales

Ensemble-based decision making to reduce model bias
Parallel execution across three top-tier LLMs
Integration with GPT-5.2, Gemini 3 Pro, and Claude Opus
Customizable model selection for specialized domain queries
Automated polling and result relay for seamless workflows
8 GitHub stars

Casos de Uso

Brainstorming creative solutions for unique technical challenges with diverse AI viewpoints
Validating critical system architecture designs through multi-model consensus
Resolving complex logic disagreements during code review or debugging