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council

Coordinate parallel AI consultants to review code and deliver weighted, filtered reports.

10|2|Updated Feb 7, 2026
Search Tags:#consensus#parallel#council#code-review#risk-analysis#ai-review
One-click install
npx skills add https://github.com/rube-de/cc-skills --skill council-rube-de
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: council
Source: https://github.com/rube-de/cc-skills/tree/main/plugins/council/skills/council
Command: npx skills add https://github.com/rube-de/cc-skills --skill council-rube-de

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating multiple AI consultants to deliver comprehensive reviews and a clear, consolidated verdict that reduces risk and accelerates decision-making.

Core Features & Use Cases

  • External AI consultant coordination for code, design, and architecture reviews
  • Parallel analysis with weighted scoring and escalation when critical issues are found
  • Structured, synthesize-able reports including location references for findings

Quick Start

Invoke the council with /council to start a parallel, multi-model review of code, plans, or architectures.

Frequently Asked Questions about council

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

FAQPage Schema
How do I get multiple AI models to review my code and reach a consensus?▼

Multi-model consensus review coordinates diverse external AI consultants in parallel to analyze code and compute weighted scores, delivering a consolidated verdict with structured findings.

Can I use parallel AI code reviews for architecture and design risk assessments?▼

Parallel AI code reviews support architecture analysis, design decisions, and risk assessments, applying security, quality, and performance filters to compute weighted scores and escalate critical findings.

How do AI council code reviews report the locations of security and performance issues?▼

AI council reviews deliver structured, synthesize-able reports that include specific location references for code findings, ensuring security and performance issues are precisely identified for resolution.

What is the best way to consolidate findings from multiple AI code reviewers?▼

Consolidating multiple AI code reviewers involves running parallel analysis, filtering results by a threshold score, and synthesizing the outputs into a single structured report covering risk and quality concerns.

Do I need to install any dependencies to run multi-model AI code reviews?▼

No dependencies are required to run multi-model AI code reviews, allowing you to directly invoke the council to coordinate external consultants and generate weighted, structured analysis reports.

When should I use a weighted AI consensus review instead of a standard single-model code review?▼

A weighted AI consensus review is ideal for PR reviews and complex architectures where reducing risk is critical, as it escalates critical issues found across multiple parallel consultants.

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