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clawrouter

Route LLM requests to the cheapest capable model across providers.

2|1|Updated Feb 13, 2026
Search Tags:#llm#multi-model#ai infrastructure#cost optimization#model routing#inference
One-click install
npx skills add https://github.com/simplefarmer69/ape-claw --skill clawrouter-simplefarmer69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clawrouter
Source: https://github.com/simplefarmer69/ape-claw/tree/main/data/forge-skills/clawhub-clawrouter
Command: npx skills add https://github.com/simplefarmer69/ape-claw --skill clawrouter-simplefarmer69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill significantly reduces AI inference costs by intelligently routing requests to the most cost-effective LLM capable of handling the task.

Core Features & Use Cases

  • Cost Savings: Achieves up to 78% reduction in LLM expenses.
  • Model Agnosticism: Supports over 30 models from major providers (OpenAI, Anthropic, Google, etc.).
  • Automated Routing: Classifies requests into tiers (SIMPLE, MEDIUM, COMPLEX, REASONING) and selects the cheapest suitable model.
  • Use Case: A startup wants to integrate LLM capabilities into their application but is concerned about high operational costs. They can use ClawRouter to ensure that simple queries are handled by inexpensive models, while complex ones are routed to more powerful, albeit pricier, options, optimizing their budget.

Quick Start

Use the clawrouter skill to set blockrun/auto as the default model.

Frequently Asked Questions about clawrouter

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

FAQPage Schema
How do I reduce LLM inference costs across multiple AI providers?▼

You can reduce LLM inference costs by dynamically routing requests to the cheapest capable model. Automated routing classifies requests into tiers like SIMPLE, MEDIUM, COMPLEX, and REASONING to achieve up to 78% cost savings.

What is the best way to route simple queries to cheaper LLM models?▼

Automated model routing routes simple queries to cheaper models by classifying requests into complexity tiers, ensuring simple queries are handled by inexpensive models while complex ones use more powerful options.

How does model routing handle requests for different AI infrastructure tiers?▼

Model routing handles AI infrastructure requests by classifying them into tiers like SIMPLE, MEDIUM, COMPLEX, and REASONING, then selecting the cheapest suitable model from over 30 supported options across major providers.

Do I need to configure specific providers for multi-model LLM routing?▼

Yes, multi-model LLM routing requires configuring model providers and routing rules, facilitating efficient AI integration by routing requests to cost-effective operations across supported providers.

Can I use model agnosticism to optimize my startup's AI application budget?▼

Model agnosticism optimizes startup AI application budgets by supporting over 30 models from major providers, ensuring simple queries use inexpensive models while complex ones route to pricier options.

When should I not use dynamic LLM request routing for cost optimization?▼

Avoid dynamic LLM request routing if your application requires a single, consistent model output without tier-based classification, as it requires configuring routing rules to function for cost-effective operation.

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