llm-router

name	llm-router
license	Apache-2.0
description	Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements. Routes cheap tasks to Haiku/GPT-4o-mini and complex tasks to Sonnet/Opus/o1. Use when deciding which model to call, optimizing LLM costs, or building multi-model agent systems. Activate on "which model", "model selection", "route to model", "LLM cost", "model routing", "cheap vs expensive model". NOT for prompt engineering (use prompt-engineer), model fine-tuning, or training custom models.
allowed-tools	Read
argument-hint	[task-description] [budget: low|medium|high]
metadata	{"category":"AI & Machine Learning","tags":["llm","router","which-model","model-selection","route-to-model"],"pairs-with":[{"skill":"cost-optimizer","reason":"Model routing is the primary mechanism for implementing cost optimization decisions"},{"skill":"cost-accrual-tracker","reason":"Real-time cost data informs routing decisions to stay within budget constraints"},{"skill":"prompt-engineer","reason":"Prompt complexity analysis determines which model tier the router should select"}]}
category	AI & Machine Learning
tags	["llm-routing","model-selection","cost-optimization","latency","fallback"]

LLM Router

Selects the optimal LLM model for each task. The single biggest cost lever in multi-agent systems — intelligent routing saves 45-85% while maintaining 95%+ of top-model quality.

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When to Use

✅ Use for:

• Deciding which model to call for a specific task
• Assigning models to DAG nodes in agent workflows
• Optimizing LLM API costs across a system
• Building cascading try-cheap-first patterns

❌ NOT for:

• Prompt engineering (use prompt-engineer)
• Model fine-tuning or training
• Comparing model architectures (academic research)

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Routing Decision Tree

flowchart TD
  A{Task type?} -->|Classify / validate / format / extract| T1["Tier 1: Haiku, GPT-4o-mini (~$0.001)"]
  A -->|Write / implement / review / synthesize| T2["Tier 2: Sonnet, GPT-4o (~$0.01)"]
  A -->|Reason / architect / judge / decompose| T3["Tier 3: Opus, o1 (~$0.10)"]
  
  T1 --> Q1{Quality sufficient?}
  Q1 -->|Yes| Done1[Use cheap model]
  Q1 -->|No| T2
  
  T2 --> Q2{Quality sufficient?}
  Q2 -->|Yes| Done2[Use balanced model]
  Q2 -->|No| T3

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Tier Assignment Table

Task Type	Tier	Models	Cost/Call	Why This Tier
Classify input type	1	Haiku, GPT-4o-mini	~$0.001	Deterministic categorization
Validate schema/format	1	Haiku, GPT-4o-mini	~$0.001	Mechanical checking
Format output / template	1	Haiku, GPT-4o-mini	~$0.001	Structured transformation
Extract structured data	1	Haiku, GPT-4o-mini	~$0.001	Pattern matching
Summarize text	1-2	Haiku → Sonnet	~$0.001-0.01	Short summaries: Haiku; nuanced: Sonnet
Write content/docs	2	Sonnet, GPT-4o	~$0.01	Creative quality matters
Implement code	2	Sonnet, GPT-4o	~$0.01	Correctness + style