Model Council Pro — 技能 — openclaw中文资讯站

技能详情（站内镜像，无评论）

Multi-model consensus system — send a query to 3+ different LLMs via OpenRouter simultaneously, then a judge model evaluates all responses and produces a win...

开发与 DevOps

许可证：MIT-0

MIT-0 ·免费使用、修改和重新分发。无需归因。

版本：v1.0.0

统计：⭐ 0 · 519 · 0 current installs · 0 all-time installs

⭐ 0

安装量（当前） 0

🛡 VirusTotal ：良性 · OpenClaw ：良性

Package：aiwithabidi/model-council-pro

安全扫描（ClawHub）

VirusTotal ：良性
OpenClaw ：良性

OpenClaw 评估

The skill's code, runtime instructions, and requested environment variable (OPENROUTER_API_KEY) are consistent with a multi-model consensus tool that calls OpenRouter; nothing requests unrelated credentials or performs unexpected local file access.

目的

Name/description claim: send queries to multiple LLMs via OpenRouter and have a judge model pick/synthesize results. Declared requirement: OPENROUTER_API_KEY. The Python script calls openrouter.ai endpoints and implements parallel calls + a judge step. All required resources align with the stated purpose.

说明范围

SKILL.md and the script only instruct the agent to call OpenRouter endpoints, aggregate responses, and have a judge model evaluate them. The code reads only the declared OPENROUTER_API_KEY env var, performs network calls to openrouter.ai, and prints or emits JSON results. It does not read arbitrary local files, other env vars, or reach out to unexpected third-party endpoints.

安装机制

No install spec; this is an instruction-only skill with an included Python script. No external archives/downloads or package installs are performed by the skill itself, minimizing install-time risk.

证书

Only OPENROUTER_API_KEY is required and used. This is proportional: the key is needed to route queries to multiple models and will be used in Authorization headers. Note: the key grants the skill the ability to make API calls (and incur costs) on the user's OpenRouter account and may expose prompts/responses to OpenRouter's logs—this is expected for the stated purpose but is an important operational/privacy consideration.

持久

always:false and default invocation settings are used. The skill does not persist or modify other skills or system configuration; it only performs runtime network calls. No elevated or persistent privileges are requested.

综合结论

This skill appears to do what it says: it uses your OPENROUTER_API_KEY to call OpenRouter and aggregate multiple model outputs. Before installing, consider: (1) Cost and billing — calls to multiple models (and a judge model) can incur non-trivial charges; monitor usage and set quotas. (2) Data exposure — prompts and model outputs are sent to OpenRouter and the upstream model providers and may be logged; avoid sending secrets or sensitive data.…

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Model Council Pro」。简介：Multi-model consensus system — send a query to 3+ different LLMs via OpenRouter…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aiwithabidi/model-council-pro/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: model-council
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": "ud83cudfdbufe0f", "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)