An intelligent routing tool that discovers Volcengine AI model capabilities and maps user requirements to the most effective endpoints.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install volcengine-ai-misc-model-discovery
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 volcengine-ai-misc-model-discovery using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Volcengine AI Model Discovery skill serves as a critical navigation layer for developers utilizing the Volcengine ecosystem. It automates the process of evaluating diverse large language models by matching specific user intents with model performance profiles. By integrating this into your workflow through Openclaw Skills, you can eliminate the manual overhead of tracking model updates and endpoint technicalities.
This skill is designed to optimize the balance between cost, speed, and output quality. It doesn't just list models; it actively analyzes constraints to provide a structured recommendation, ensuring that your AI agents operate with the most suitable intelligence available in the Volcengine catalog.
To deploy this model discovery logic, ensure your environment is prepared for Openclaw Skills integration.
# Install the Openclaw CLI if not already present
npm install -g @openclaw/cli
# Initialize the Volcengine discovery skill
openclaw skill add volcengine-ai-misc-model-discovery
Configure your Volcengine access credentials in your environment:
export VOLC_ACCESS_KEY="your_access_key"
export VOLC_SECRET_KEY="your_secret_key"
export VOLC_REGION="your_preferred_region"
The skill organizes its model routing intelligence using the following schema:
| Attribute | Description |
|---|---|
| Task Type | The categorized intent (e.g., classification, generation, analysis). |
| Candidate Endpoints | A curated list of models capable of fulfilling the request. |
| Recommended Endpoint | The optimal model identified by the scoring logic. |
| Tradeoffs | A summary of quality vs. cost vs. latency for the recommended model. |
| Invocation Template | A pre-formatted code snippet for calling the selected endpoint. |
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