| 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
- Stage 1: Opinion Collection - Each member (LLM) responds independently
- Stage 2: Peer Review - Anonymized responses are mutually ranked
- Stage 3: Synthesis - Chairman integrates all opinions and reviews into final response
Quick Start
python scripts/run.py council_skill.py "What's the optimal caching strategy?"
python scripts/run.py cli.py --dashboard "What's the optimal caching strategy?"
python scripts/run.py council_skill.py --dry-run "Fix the bug in buggy.py"
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
- Create
scripts/.env to configure models
- Install and configure OpenCode CLI
- Run
python scripts/run.py council_skill.py --setup for details
Resources
See README.md for more details.
