/llm_council_skill
Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis
$ npx -y skills add shuntacurosu/llm_council_skill --skill llm_council_skill --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/llm_council_skill
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Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis
SKILL.md
llm_council_skill.SKILL.mdname: 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.
Read more
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.
A Claude Skill that orchestrates multiple LLMs as a "council" to achieve collective intelligence through peer review and synthesis.