model-router-premium
Route model requests based on configured models, costs and task complexity. Use for routing general/low-complexity requests to the cheapest available model and for higher-complexity requests to stronger models.
Why use this skill?
Optimize LLM usage with the Model Router Premium. Automatically route tasks to the most cost-effective AI models based on complexity and performance requirements.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/mrjootta/model-router-premiumWhat This Skill Does
The model-router-premium skill is a sophisticated decision-making engine designed to optimize LLM selection for the OpenClaw ecosystem. Rather than hard-coding model endpoints, this tool acts as a middleware layer that evaluates the nature of an incoming prompt. It leverages a configurable heuristic system to analyze task complexity—distinguishing between straightforward, short-form queries and high-fidelity, resource-intensive tasks. By maintaining a registry of model capabilities, cost scores, and performance metadata, the router ensures that your application utilizes the most cost-effective model for the job without sacrificing quality. The architecture is built on deterministic logic, meaning every routing decision is auditable and consistent, preventing the common issue of over-spending on simple routine operations.
Installation
To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal:
clawhub install openclaw/skills/skills/mrjootta/model-router-premium
Ensure that you have your models.json configuration file prepared in your workspace, as the script requires this file to map available infrastructure. You can reference the provided examples/models.json to structure your provider credentials and model rankings correctly.
Use Cases
This skill is ideal for production environments where API costs are a primary concern. It is best suited for:
- Middleware for high-traffic server applications: Automatically route simple chat history summaries to low-cost models while pushing complex reasoning tasks to high-tier models.
- Batch processing: Efficiently sort large sets of prompts based on the expected computational requirements.
- Dynamic LLM orchestration: Swap model providers or upgrade/downgrade model versions in your configuration file without modifying your primary application codebase.
Example Prompts
- "Route the request 'Draft a quick reply to this meeting invite' using the auto-router logic."
- "Analyze the complexity of this technical documentation and suggest the optimal model from my models.json list."
- "Summarize this 50-page research paper: should I use the premium-tier model or a lightweight alternative?"
Tips & Limitations
The router's effectiveness is strictly tied to the quality of your models.json file. Ensure that your cost_score values are normalized across different providers to allow for accurate comparisons. The current heuristic for task complexity is based on input length and structural cues; for highly specialized tasks (like complex code refactoring vs. creative writing), you may need to extend the router logic. Note that while this tool reduces operational overhead, it does not manage the actual execution of the models; it only returns the optimal configuration for you to initiate the call.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-mrjootta-model-router-premium": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read