Route any prompt to the highest-performing LLM based on thousands of peer-reviewed council deliberations.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install llmcouncil-router
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install llmcouncil-router using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
LLM Council Router is a sophisticated routing engine designed to eliminate the trial-and-error approach to model selection. By leveraging thousands of peer-reviewed council deliberations, this tool identifies the top-performing large language model for any specific query. It provides developers with a data-backed recommendation engine, ensuring that tasks are always handled by the most capable model available.
As part of the ecosystem of Openclaw Skills, this tool acts as an intelligent middleware layer. It doesn't just guess which model is better; it analyzes real evaluation data to return a specific model ID, relevance score, and confidence level, allowing for high-precision automation in complex AI workflows.
To utilize this skill, you must first obtain an API key from the LLM Council developer portal. Once obtained, set your environment variable to authenticate your requests.
export LLMCOUNCIL_API_KEY='clwb_YOUR_KEY_HERE'
You can then trigger a route request using a simple curl command:
curl -X POST https://clawbot.llmcouncil.ai/v1/route \
-H "Content-Type: application/json" \
-H "X-API-Key: $LLMCOUNCIL_API_KEY" \
-d '{"query": "Explain quantum entanglement simply"}'
The LLM Council Router returns structured data to facilitate easy integration with other Openclaw Skills and model providers.
| Property | Description |
|---|---|
model |
The recommended model string, formatted for direct use with OpenRouter. |
relevance |
A float (0-1) indicating how closely the query matches historical evaluation data. |
confidence |
A float (0-1) representing the margin by which the top model won against competitors. |
model_rankings |
An array of objects detailing the performance of all considered models. |
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