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.
git clone https://github.com/decolua/9router.git9router 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.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/decolua/9routerCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
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.
# 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)
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