claude-router

🥈Silver

Intelligent model orchestration for Claude Code - routes queries to optimal Claude model (Haiku/Sonnet/Opus) based on complexity. It also includes many more features. If this project is working well for you and would like to support me, just help spread the word. Thanks!

3530Updated 1mo ago

Intermediate30min to implementautomation

Saves ~90 min per use

Quick InstallView Source

git clone https://github.com/0xrdan/claude-router.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Identify the Task**: Clearly define the task you need to accomplish, including its scope, complexity, and output requirements. Use specific criteria like 'depth of analysis,' 'speed,' or 'output quality' to guide the router. 2. **Run the Router**: Paste the task description into your Claude Code environment or chat interface with the claude-router skill enabled. Specify any additional criteria (e.g., 'prioritize speed over depth') to refine the recommendation. 3. **Review the Recommendation**: The router will suggest the optimal model(s) and a step-by-step breakdown. Verify that the justification aligns with your needs. Adjust criteria if the recommendation doesn't fit. 4. **Execute the Task**: Use the recommended model(s) for each step. For multi-step tasks, delegate subtasks to the appropriate models (e.g., Opus for analysis, Haiku for formatting). 5. **Optimize Iteratively**: After completing the task, review the output. If the results are suboptimal, tweak the criteria (e.g., 'prioritize visual quality') and rerun the router to refine the approach.

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/0xrdan/claude-router

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 claude-router for [TASK_DESCRIPTION]. Analyze the complexity of this task and recommend the optimal Claude model (Haiku/Sonnet/Opus) to handle it. Justify your recommendation based on [CRITERIA: e.g., depth of analysis, speed requirements, output quality]. If the task involves multiple steps, specify how to break it down across models for efficiency.

Example Output

For the task of 'Analyze the Q3 financial performance of Acme Corp and draft a 10-page report with visualizations,' I recommend using **Claude Opus** for the following reasons:

1. **Depth of Analysis**: The task requires synthesizing financial data, identifying trends, and generating insights—all of which demand Opus's advanced reasoning capabilities. Haiku would struggle with the nuance, and Sonnet might lack the depth for a comprehensive report.

2. **Output Quality**: Opus excels at structured, high-quality outputs, which is critical for a 10-page report. It can handle complex formatting, integrate visualizations (e.g., charts, tables), and ensure coherence across sections.

3. **Efficiency Breakdown**:
- **Step 1 (Opus)**: Parse and analyze the financial data (balance sheets, income statements, cash flow). Identify key metrics like revenue growth, profit margins, and debt-to-equity ratios.
- **Step 2 (Sonnet)**: Draft the initial report structure, including executive summary, key findings, and recommendations.
- **Step 3 (Haiku)**: Generate visualizations (e.g., bar charts for revenue trends, pie charts for expense breakdowns) and format the final document.

**Why not Sonnet?** While Sonnet is capable, it may not deliver the same level of detail or handle the full scope of the task without additional iterations. Haiku is too limited for this use case.

**Pro Tip**: If you're using this in Claude Code, pre-load the financial data into a variable (e.g., `financial_data = load_csv('q3_financials.csv')`) to speed up the analysis phase.