/llm_council_skill

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.