Agent skill

LLM Council

Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis

View SKILL.md on GitHub Repository
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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/llm-council-skill

SKILL.md

Overview

LLM Council is a Skill that organizes multiple LLMs as "council members" and generates high-quality responses through a 3-stage process.

Use Cases

  • When you need multiple perspectives for important decisions
  • When you want multiple AIs to review code
  • When comparing and evaluating design proposals
  • When you need objective responses with reduced bias

3-Stage Process

  1. Stage 1: Opinion Collection - Each member (LLM) responds independently
  2. Stage 2: Peer Review - Anonymized responses are mutually ranked
  3. Stage 3: Synthesis - Chairman integrates all opinions and reviews into final response

Quick Start

bash
# Basic question
python scripts/run.py council_skill.py "What's the optimal caching strategy?"

# With TUI dashboard
python scripts/run.py cli.py --dashboard "What's the optimal caching strategy?"

# Code fix (diff only)
python scripts/run.py council_skill.py --dry-run "Fix the bug in buggy.py"

# Auto-merge
python scripts/run.py council_skill.py --auto-merge "Add error handling"

Command Options

Option Description
--dashboard, -d TUI dashboard for real-time monitoring
--worktrees Git worktree mode - each member works independently
--dry-run Show diff without merging
--auto-merge Auto-merge the top-ranked proposal
--merge N Merge member N's proposal
--confirm Show confirmation prompt before merge
--no-commit Apply changes without staging
--list Show conversation history
--continue N Continue conversation N

Setup

  1. Create scripts/.env to configure models
  2. Install and configure OpenCode CLI
  3. Run python scripts/run.py council_skill.py --setup for details

Resources

See README.md for more details.

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