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agent-router

Analyze task characteristics and constraints to recommend AI models with fallbacks.

Updated Feb 25, 2026
Search Tags:#cost optimization#llm routing#model selection#task classification#ai routing#prompt routing
One-click install
npx skills add https://github.com/sky770825/NEUXA- --skill agent-router-sky770825
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-router
Source: https://github.com/sky770825/NEUXA-/tree/main/quarantine/skills-slim-20260213/agent-router
Command: npx skills add https://github.com/sky770825/NEUXA- --skill agent-router-sky770825

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually selecting the right AI model for different tasks leads to wasted costs, slow results, and poor output quality when using mismatched models for coding, content creation, analysis, or other AI-powered work.

Core Features & Use Cases

  • Intelligent Task Classification: Automatically categorizes tasks by type (coding, summarization, translation, etc.) and complexity (low, medium, high) to align with model strengths.
  • Cost & Speed Optimization: Balances model performance with user-defined budget, speed, and context length constraints to maximize value.
  • Fallback & Edge Case Handling: Auto-routes to alternative models if the primary option is rate-limited, and accounts for multi-modal tasks, long context needs, and ambiguous task types.
  • Use Case: For a team running a mix of code generation, invoice summarization, and multilingual translation tasks, this skill automatically assigns each task to the most cost-effective model without manual selection.

Quick Start

Use the agent-router skill to get a recommended AI model for your current task, including a fallback option and estimated cost tier.

Frequently Asked Questions about agent-router

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does LLM routing help optimize AI model selection for diverse tasks?▼

LLM routing analyzes task characteristics, complexity, and user constraints like cost and speed to automatically match tasks with the most suitable AI model. This prevents wasted budget and poor output quality from manual model selection mismatches.

What is the best way to reduce AI model costs for mixed workloads like coding and translation?▼

To reduce AI model costs for mixed workloads, apply task classification to categorize complexity and assign each job to the most cost-effective model. This balances performance with your budget and speed constraints to maximize value across diverse operations.

Can I use automated prompt routing to handle rate limits and context overflow?▼

Yes, automated prompt routing includes fallback model recommendations to handle rate limits and edge cases like context overflow. It auto-routes tasks to alternative models to ensure reliable task completion when the primary option fails.

How do I route multi-modal tasks to the right large language model?▼

Multi-modal tasks are routed by evaluating task characteristics alongside user-defined constraints including context length requirements. The routing framework accounts for these edge cases to provide primary and fallback model recommendations for reliable completion.

Does AI routing work for high complexity software development and content creation tasks?▼

AI routing works for software development and content creation by classifying task complexity as low, medium, or high and aligning it with model strengths. It ensures reliable task completion by matching the specific demands of your workflow.

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