LLM Council

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.