9router

🥈Silver

9router is an AI proxy that routes requests to multiple AI models like Claude Code, Codex, Cursor, OpenAI, Gemini, and Copilot. It benefits developers and operations teams by abstracting away model-specific APIs, enabling seamless integration into existing workflows and tools.

528430Updated Today

Intermediate30min to implementautomation

Saves ~150 min per use

Quick InstallView Source

git clone https://github.com/decolua/9router.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/decolua/9router

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Act as a universal AI proxy. Route the following request to the most appropriate AI tool among Claude Code, Codex, Cursor, OpenAI, Claude, Gemini, or Copilot. [COMPANY] wants to [TASK]. Use [DATA] to accomplish this. Provide a detailed response with step-by-step instructions.

Example Output

# AI Proxy Response

## Selected AI Tool: Codex

### Analysis
Based on the request to automate data analysis and visualization for [COMPANY]'s marketing department, Codex is the most suitable tool due to its strong capabilities in data manipulation and scripting.

### Solution

1. **Data Cleaning**: 
   - Removed 15% of duplicate entries from the [DATA] dataset.
   - Standardized date formats across all records.

2. **Analysis**: 
   - Identified a 23% increase in customer engagement during Q3.
   - Detected a correlation between engagement and promotional email frequency.

3. **Visualization**: 
   - Generated interactive dashboards showing engagement trends.
   - Created a heatmap of customer interactions by region.

### Recommendations
- Schedule promotional emails every 10 days to maintain engagement.
- Focus marketing efforts on regions with low engagement scores.

### Code Snippet
```python
import pandas as pd
import seaborn as sns

# Load and clean data
data = pd.read_csv('[DATA]')
data.drop_duplicates(inplace=True)
```