llm-council

Multi-agent brainstorming improves decision support by running uncertain choices through five independent AI advisor styles, identifying blind spots via anonymous peer review, and synthesizing a single actionable verdict. This prevents the unreliable bias of single-perspective AI answers.

To stress-test a product positioning decision, ask the AI council to analyze the choice and provide your context, options, and constraints. The multi-agent workflow applies contrarian, first principles, and execution-focused perspectives to generate a balanced recommendation.

Yes, multi-agent strategy analysis suits hiring decisions, business pivots, and prioritization scenarios. The workflow applies coordinated AI perspectives and structured verdict generation to high-stakes tradeoffs requiring critical evaluation and balanced judgment.

Evaluating business planning tradeoffs is best handled by applying multiple AI perspectives including expansion and outsider viewpoints. The council compares independent responses, identifies blind spots through anonymous peer review, and creates a chairman-style verdict with actionable guidance.

You do not need specific dependencies or components to run peer review analysis, but you must provide the relevant workspace context, options, and constraints. The Skill analyzes the provided decision scenario using independent perspectives without requiring external tools.