task-complexity-router

Name: task-complexity-router
Availability: InStock
Author: MadAppGang

Route tasks to AI model tiers based on complexity using keyword heuristics.

7|4|Updated Feb 26, 2026

Search Tags:#routing #cost optimization #ai orchestration #model selection #task analysis #llm tiering

Ready to run

npx skills add https://github.com/MadAppGang/magus --skill task-complexity-router

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: task-complexity-router
Source: https://github.com/MadAppGang/magus/tree/main/plugins/multimodel/skills/task-complexity-router
Command: npx skills add https://github.com/MadAppGang/magus --skill task-complexity-router

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill intelligently routes tasks to the most appropriate AI model tier based on complexity, significantly reducing API costs and optimizing processing speed.

Core Features & Use Cases

Tiered Model Routing: Utilizes a 4-tier system (Native Tools, Haiku, Sonnet, Opus) to match task complexity with model capabilities.
Cost Optimization: Achieves 60-90% savings on AI API costs by reserving premium models for complex tasks.
Use Case: When asked to "add comments to a function," the skill routes it to a fast, cheap model (Haiku). If asked to "design a microservices architecture," it routes to a powerful, expensive model (Opus).

Quick Start

Use the task-complexity-router skill to analyze the complexity of the request "implement user authentication".

Frequently Asked Questions about task-complexity-router

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

FAQPage Schema

How do I reduce AI API costs by routing tasks to different model tiers?▼

AI API cost reduction is achieved by routing tasks to different model tiers based on complexity. This skill uses keyword and context heuristics to match simple tasks to cheaper models and complex tasks to premium LLMs, saving 60-90% on costs.

What is LLM tiering and how does it optimize model selection?▼

LLM tiering categorizes AI models into capability levels to optimize model selection. This skill uses a 4-tier system ranging from native tools and Haiku to Sonnet and Opus, matching task requirements to the appropriate tier for cost efficiency and speed.

Can I use dynamic tier escalation for multi-agent coordination systems?▼

Dynamic tier escalation for multi-agent coordination systems is supported. The skill analyzes task requirements and can escalate to higher model tiers when needed, while integrating with multi-agent coordination workflows to manage cost tracking.

What's the best way to analyze task complexity for AI orchestration?▼

The best way to analyze task complexity for AI orchestration is using keyword and context heuristics. This skill evaluates task requirements to select optimal model tiers, ensuring simple requests like adding comments use fast models while architecture design uses premium LLMs.

Does AI model routing work without external dependencies?▼

AI model routing works without external dependencies. The skill operates independently using internal references to analyze task complexity and route requests across the 4-tier system, from native tools to premium models like Claude Opus.

When should I not use automated task complexity routing?▼

Automated task complexity routing should not be used when precise manual control over specific model selection is required. The skill relies on keyword and context heuristics, which may not perfectly align with specialized routing preferences for unique edge cases.