@shuntacurosu/llm-council

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name: LLM Council description: Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis version: 1.0.0 dependencies: python>=3.8, python-dotenv, loguru

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

# 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.