LLM Council - Claude Skill

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A Claude Skill that orchestrates multiple LLMs as a "council" to achieve collective intelligence through peer review and synthesis.

Overview

LLM Council organizes multiple LLMs as a "council" instead of querying a single LLM, deriving conclusions through a 3-stage process:

1. Stage 1: Opinion Collection - Each council member (LLM) independently responds to the query
2. Stage 2: Peer Review - Each member anonymously reviews and ranks other members' responses
3. Stage 3: Final Synthesis - The Chairman LLM integrates all opinions and reviews into the final response

Key Features

• Git Worktree Integration: Manage each member's work in independent git worktrees
• Anonymous Review: Member identities are anonymized in Stage 2 for fair evaluation
• OpenCode CLI Integration: Generate and edit code directly within worktrees
• Flexible Configuration: Freely configure member models and chairman model
• Real-time Dashboard: TUI dashboard showing member status, stage progress, and live logs
• Conversation History: Save all council sessions in JSON format

Dashboard

Monitor council sessions in real-time with the built-in TUI dashboard:

The dashboard displays:

• Stage Flow: Visual progress indicator showing current stage ([1] Responses ━━▶ [2] Rankings ━━▶ [3] Synthesis)
• Member Status: Real-time status of each council member (🟢 Active, ⏳ Waiting, ✅ Completed, ❌ Error)
• Live Logs: Latest 15 log entries scrolling in real-time
• Statistics: API call count, errors, and session info

Enable with --dashboard or -d flag:

python scripts/run.py cli.py --dashboard "Your question"

Setup

1. Environment Variables

Create a scripts/.env file to configure the models:

cd scripts
cp .env.example .env

Edit .env:

# Council Members - comma-separated provider/model list
# Format: opencode/provider/model (or provider/model, defaults to opencode)
# Examples:
#   opencode/openrouter/openai/gpt-4
#   opencode/anthropic/claude-3-5-sonnet-20241022
#   anthropic/claude-3-5-sonnet-20241022  (opencode prefix can be omitted)
COUNCIL_MODELS=opencode/openai/gpt-4,opencode/anthropic/claude-3-5-sonnet,opencode/google/gemini-pro

# Chairman Model - performs final synthesis
CHAIRMAN_MODEL=opencode/anthropic/claude-3-5-sonnet

# Title Generation Model - for conversation titles (optional, defaults to CHAIRMAN_MODEL)
# TITLE_MODEL=opencode/anthropic/claude-3-5-haiku-20241022

# Dashboard Settings (optional)
DASHBOARD_TIMEOUT=5       # Seconds to show dashboard after completion
DASHBOARD_REFRESH_RATE=10 # Dashboard refresh rate in Hz

About OpenCode CLI

This tool uses OpenCode CLI to interact with LLMs. OpenCode must be installed and properly configured.

Note: Future support for other CLI tools like claude-code and codex is planned.

2. Virtual Environment (Automatic)

A virtual environment is automatically created on first run with dependencies installed.

To manually set up the virtual environment:

python scripts/setup_environment.py

3. Git Repository

This tool uses git worktrees, so it must be run within a git repository.

Usage

Basic Usage (Recommended)

Using run.py automatically sets up the virtual environment and runs the script with the correct Python environment:

python scripts/run.py council_skill.py "Enter your question here"

Example:

python scripts/run.py council_skill.py "What is the best approach to implement caching in a web application?"

Command Line Options

Basic Options

Option	Description	Example
query	Question to send to the council (positional)	"Your question"
--dashboard, -d	Enable TUI dashboard for real-time monitoring	--dashboard
--worktrees	Enable Git worktree mode	--worktrees
--list	Show conversation history	--list
--show N	Show details of conversation N	--show 1
--continue N	Continue conversation N	--continue 1 "Follow-up"
--setup	Show setup guide	--setup

Merge Options (with --worktrees)

Option	Description	Example
--auto-merge	Auto-merge the top-ranked proposal	--auto-merge
--merge N	Merge member N's proposal	--merge 2
--dry-run	Show diff without merging	--dry-run
--confirm	Show confirmation before merge	--auto-merge --confirm
--no-commit	Apply without staging	--auto-merge --no-commit

Examples

# Basic question
python scripts/run.py council_skill.py "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"

# Code fix (auto-merge with confirmation)
python scripts/run.py council_skill.py --auto-merge --confirm "Add error handling to divide function"

# Code fix (auto-merge without commit)
python scripts/run.py council_skill.py --auto-merge --no-commit "Add tests"

# Merge specific member's proposal
python scripts/run.py council_skill.py --merge 2 "Refactor this"

# Continue conversation
python scripts/run.py council_skill.py --continue 1 "Tell me more"

View Conversation History

python scripts/run.py council_skill.py --list

Show Setup Guide

python scripts/run.py council_skill.py --setup

Check Virtual Environment Status

python scripts/setup_environment.py --check

Project Structure

llm_council/
├── .venv/                     # Virtual environment (auto-created)
├── scripts/
│   ├── __init__.py
│   ├── run.py                 # Run scripts via virtual environment
│   ├── setup_environment.py   # Virtual environment setup
│   ├── council_skill.py       # Main entry point (backward compatible)
│   ├── api.py                 # High-level API (for dashboard)
│   ├── cli.py                 # CLI interface
│   ├── config.py              # Configuration management
│   ├── council.py             # 3-stage council logic
│   ├── dashboard.py           # TUI dashboard (Rich-based)
│   ├── worktree_manager.py    # Git worktree management
│   ├── unified_client.py      # Unified LLM client
│   ├── opencode_client.py     # OpenCode CLI client
│   ├── storage.py             # Conversation history storage
│   ├── logger.py              # Logging configuration
│   ├── .env                   # Environment variables (create this)
│   ├── .env.example           # Environment variables template
│   ├── data/
│   │   ├── conversations/     # Conversation history JSON
│   │   └── logs/              # Execution logs
│   ├── prompts/
│   │   └── templates.py       # Prompt templates
│   └── worktrees/             # Git worktree directory
├── requirements.txt           # Python dependencies
├── skill.json                 # Claude Skill definition
└── README.md                  # This file

How It Works

Stage 1: Opinion Collection

Each council member (configured LLM model) independently responds to the user's query.

• Normal mode: Text-based responses
• Worktree mode: Each member works in an independent worktree to generate code changes

Stage 2: Peer Review

All members anonymously review other members' responses.

• Responses are anonymized as "Response A", "Response B", etc.
• Each member evaluates and ranks all responses
• Aggregate scores are calculated to generate overall rankings

Stage 3: Final Synthesis

The chairman model integrates all opinions and review results to generate the final response.

• Considers strengths of each member's opinion
• Reflects issues identified in peer review
• Analyzes consensus patterns and differences
• Provides a clear final response representing the council's collective intelligence

Customization

Changing Council Members

Edit COUNCIL_MODELS in scripts/.env:

COUNCIL_MODELS=opencode/openai/gpt-4-turbo,opencode/anthropic/claude-3-opus,opencode/google/gemini-pro

Changing Chairman Model

Edit CHAIRMAN_MODEL in scripts/.env:

CHAIRMAN_MODEL=opencode/anthropic/claude-3-5-sonnet

Customizing Prompts

Edit scripts/prompts/templates.py to customize prompts for each stage.

Troubleshooting

"not a git repository" Error

This tool must be run within a git repository. Initialize with git init.

Removing Worktrees

If worktrees remain for some reason:

git worktree prune

Or manually:

rm -rf scripts/worktrees/*
git worktree prune

License

MIT License

Acknowledgments

This project is inspired by Andrej Karpathy's llm-council.

Future Plans

• OpenCode tool integration (command-based LLM interface)
• Real-time progress display
• TUI Dashboard with Rich
• More detailed member settings (temperature parameters, expertise areas, etc.)
• Web interface (optional)