---
name: model-council-f2e3f2340658
description: Multi-model consensus system — send a query to 3+ different LLMs via OpenRouter simultaneously, then a judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer. Like having a board of AI advisors. Use for important decisions, code review, research verification.
homepage: https://www.agxntsix.ai
license: MIT
compatibility: Python 3.10+, OpenRouter API key
metadata: {"openclaw": {"emoji": "\ud83c\udfdb\ufe0f", "requires": {"env": ["OPENROUTER_API_KEY"]}, "primaryEnv": "OPENROUTER_API_KEY", "homepage": "https://www.agxntsix.ai"}}
---

# Model Council 🏛️

**Get consensus from multiple AI models on any question.**

Send your query to 3+ different LLMs simultaneously via OpenRouter. A judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer.

## When to Use

- **Important decisions** — Don't trust one model's opinion
- **Code review** — Get multiple perspectives on architecture choices
- **Research verification** — Cross-check facts across models
- **Creative work** — Compare writing styles and pick the best
- **Debugging** — When one model is stuck, others might see the issue

## How It Works

```
Your Question
    ├──→ Claude Sonnet 4    ──→ Response A
    ├──→ GPT-4o             ──→ Response B
    └──→ Gemini 2.0 Flash   ──→ Response C
                                    │
                              Judge (Opus) evaluates all
                                    │
                              ├── Winner + Reasoning
                              ├── Synthesized Best Answer
                              └── Cost Breakdown
```

## Quick Start

```bash
# Basic usage
python3 {baseDir}/scripts/model_council.py "What's the best database for a real-time analytics dashboard?"

# Custom models
python3 {baseDir}/scripts/model_council.py --models "anthropic/claude-sonnet-4,openai/gpt-4o,google/gemini-2.5-pro" "Your question"

# Custom judge
python3 {baseDir}/scripts/model_council.py --judge "openai/gpt-4o" "Your question"

# JSON output
python3 {baseDir}/scripts/model_council.py --json "Your question"

# Set max tokens per response
python3 {baseDir}/scripts/model_council.py --max-tokens 2000 "Your question"
```

## Configuration

| Flag | Default | Description |
|------|---------|-------------|
| `--models` | claude-sonnet-4, gpt-4o, gemini-2.0-flash | Comma-separated model list |
| `--judge` | anthropic/claude-opus-4-6 | Judge model |
| `--max-tokens` | 1024 | Max tokens per council member |
| `--json` | false | Output as JSON |
| `--timeout` | 60 | Timeout per model (seconds) |

## Environment

Requires `OPENROUTER_API_KEY` environment variable.

## Output Example

```
═══ MODEL COUNCIL RESULTS ═══

Question: What's the best way to handle auth in a microservices architecture?

── Council Member Responses ──

🤖 anthropic/claude-sonnet-4 ($0.0043)
Use a centralized auth service with JWT tokens...

🤖 openai/gpt-4o ($0.0038)
Implement OAuth 2.0 with an API gateway...

🤖 google/gemini-2.0-flash-001 ($0.0012)
Consider using service mesh with mTLS...

── Judge Verdict (anthropic/claude-opus-4-6, $0.0125) ──

🏆 Winner: anthropic/claude-sonnet-4
Reasoning: Most comprehensive and practical approach...

📝 Synthesized Answer:
The best approach combines elements from all three...

💰 Total Cost: $0.0218
```

## Credits
Built by [M. Abidi](https://www.linkedin.com/in/mohammad-ali-abidi) | [agxntsix.ai](https://www.agxntsix.ai)
[YouTube](https://youtube.com/@aiwithabidi) | [GitHub](https://github.com/aiwithabidi)
Part of the **AgxntSix Skill Suite** for OpenClaw agents.

📅 **Need help setting up OpenClaw for your business?** [Book a free consultation](https://cal.com/agxntsix/abidi-openclaw)


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/model-council-pro/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/model-council-pro/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Marketing, Product Management
Roles: Data Scientist, Growth Marketer, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API
Tools: OpenAI API, Openrouter
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 37f800bfe85459281aae62fcc591a224e953f2986f42f4b2fc4ea0f5a4531113