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llm-council

Run multi-advisor peer review to validate high-stakes decisions.

Updated May 22, 2026
Search Tags:#decision-making#multi-perspective#peer-review#ai-council#pressure-test#strategic-verdict
One-click install
npx skills add https://github.com/shekerkamma/peopletech-marketplace --skill llm-council-shekerkamma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-council
Source: https://github.com/shekerkamma/peopletech-marketplace/tree/main/plugins/ai-strategy/skills/llm-council
Command: npx skills add https://github.com/shekerkamma/peopletech-marketplace --skill llm-council-shekerkamma

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Single-perspective AI answers to high-stakes decisions are often confidently wrong, leaving you vulnerable to costly mistakes you could have avoided with more diverse, rigorous analysis.

Core Features & Use Cases

  • 5 Specialized Advisor Perspectives: Runs your decision through advisors focused on fatal flaws, root problem reframing, upside opportunities, outsider objectivity, and operational feasibility to cover all critical angles.
  • Anonymous Peer Review: Advisors evaluate each other's outputs without knowing the author, eliminating bias toward preferred thinking styles.
  • Structured Verdict Delivery: The chairman synthesizes all inputs into a clear output with consensus points, clashing perspectives, missed blind spots, a concrete recommendation, and a single first step.
  • Use Case: For example, if you're deciding between Azure OpenAI and AWS Bedrock for a client POC, the council will surface hidden integration risks, unconsidered cost upside, and Monday-morning implementation blockers you'd miss from a single AI response.

Quick Start

Ask the llm-council skill to pressure-test your decision between [your two options] including all relevant context about your use case, constraints, and what's at stake.

Frequently Asked Questions about llm-council

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

FAQPage Schema
How do I pressure-test high-stakes decisions to avoid single-perspective AI errors?▼

To pressure-test high-stakes decisions, you need a multi-perspective peer review process that evaluates tradeoffs from diverse angles. This approach runs your decision through specialized advisors focused on fatal flaws, operational feasibility, and upside opportunities to eliminate blind spots.

What is multi-perspective AI peer review and how does it work?▼

Multi-perspective AI peer review is a validation mechanism where specialized advisors anonymously evaluate each other's outputs to eliminate bias. It synthesizes conflicting perspectives and consensus points into a structured strategic verdict with a concrete recommendation and a single first step.

How do I validate vendor selection and architecture choices using AI?▼

You validate vendor selection and architecture choices by submitting your options, constraints, and stakes to a multi-advisor review council. The council surfaces hidden integration risks, unconsidered cost upside, and implementation blockers to produce a clear actionable recommendation.

Can I use multi-advisor review for pricing model pivots and hiring versus automation decisions?▼

Yes, you can use multi-advisor review for pricing model pivots and hiring versus automation decisions. The peer review process applies to business, technical, and strategic tradeoffs by reframing root problems and evaluating operational feasibility to deliver a structured verdict.

What is the best way to identify blind spots in strategic tradeoffs?▼

The best way to identify blind spots in strategic tradeoffs is through anonymous peer review across five specialized advisor perspectives. This method highlights missed blind spots and clashing perspectives by applying outsider objectivity and fatal flaw analysis to your decision.

When do I need a structured verdict for my business decisions?▼

You need a structured verdict for your business decisions when facing high-stakes tradeoffs where single-perspective analysis might be confidently wrong. It delivers consensus points, conflicting perspectives, and a concrete actionable recommendation paired with a single first step to prevent costly mistakes.

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