agent-council_skill

Overview

This skill collects opinions from multiple AI agents and synthesizes them into a concise, actionable response. It is designed for situations where you want diverse perspectives, consensus checks, or a summarized judgment from several models. The tool runs agents in parallel, gathers outputs, and creates a synthesized answer with optional provenance and member breakdowns.

How this skill works

You submit a single question or job and the skill dispatches it to a configurable set of member agents. Each agent returns its response and metadata; the skill then analyzes those responses to identify agreement, divergence, and supporting reasons. Finally, it produces a synthesized summary that highlights consensus points, disagreements, and recommended next steps. Jobs can be run interactively (one-shot) or as tracked jobs with results and cleanup steps.

When to use it

• When you want multiple independent AI perspectives on a single question.
• To validate an answer by checking for consensus across models.
• When complex judgments benefit from seeing supporting arguments and dissenting views.
• To generate a synthesized recommendation for stakeholders from diverse agent inputs.
• During research, design reviews, or risk assessments where multiple viewpoints reduce bias.

Best practices

• Configure a diverse set of member agents to maximize varied viewpoints.
• Provide clear, specific prompts so members address the same scope and constraints.
• Use job mode for longer tasks to capture provenance and compare outputs reliably.
• Review member metadata to understand which agents influenced the synthesis.
• Limit the number of simultaneous members to balance depth of responses and cost.

Example use cases

• Summon the council to get product feature trade-off recommendations from several models.
• Ask other AIs to critique a marketing message and synthesize improvements.
• Collect safety and risk assessments from multiple agents before releasing a policy.
• Use as a consensus check when a single model returns uncertain or conflicting advice.
• Run periodic audits of model behavior by comparing outputs across member set.