A CLI-powered discovery tool to explore, filter, and export Venice AI's comprehensive catalog of generative models and capabilities.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install venice-models
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install venice-models using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Venice Models?
The Venice Models skill provides a seamless way to interface with the Venice AI ecosystem directly from your terminal. As part of the Openclaw Skills collection, it allows developers and researchers to query available LLMs, image generators, and specialized tools like TTS or ASR without needing an API key for discovery.
This utility is essential for developers building multi-modal applications who need real-time data on model capabilities, context windows, and pricing structures. By providing a clean interface to the Venice AI public API, this skill streamlines the process of selecting the right model for any specific task, from text embeddings to high-resolution video generation.
Venice Models Use Cases
- Identifying the most cost-effective LLMs for chat or reasoning tasks.
- Filtering for specific generative capabilities like image or video generation.
- Automating model selection in scripts by exporting model names in a clean format.
- Checking context window limits and pricing for RAG-based applications.
- Integration into Openclaw Skills workflows that require dynamic model discovery.
How Venice Models Works
- The user executes the discovery script using the uv package manager, which handles all Python dependencies automatically.
- The skill sends a request to the public Venice AI models endpoint, requiring no authentication for listing tasks.
- Users apply optional flags to filter by specific model types such as text, image, code, or speech recognition.
- The skill processes the API response and formats it into the user's choice of tables, JSON, or plain text names.
- The resulting data is displayed in the terminal or saved to a local file for integration with other Openclaw Skills.
Venice Models Setup
To get started with this skill from the Openclaw Skills library, ensure you have uv installed.
# Install uv if you haven't already
brew install uv
# Run the model discovery script directly
uv run scripts/models.py
No API keys are required to list models, making this one of the most accessible Openclaw Skills for initial exploration.
Venice Models Data Schema & Taxonomy
The skill retrieves and organizes structured data regarding model traits and availability using the following schema:
| Property | Description |
|---|---|
| MODEL ID | The unique identifier used for API calls (e.g., llama-3.3-70b). |
| TYPE | The functional category (text, image, video, tts, asr, etc.). |
| CONTEXT | The maximum token limit for LLM-based models. |
| PRICING | The cost per million tokens or per generation unit. |
Venice Models Advanced Features
- Support for multiple output formats including interactive tables, JSON objects, and raw model names for easy shell piping.
- Granular type-specific filtering to narrow down specialized models like code-optimized LLMs or image upscalers.
- Zero-configuration execution using PEP 723 inline script metadata for automated dependency management via uv.
- Direct file export capabilities to save model metadata to local JSON files for version-controlled documentation.
- Seamless integration with the broader Openclaw Skills ecosystem for automated model discovery in complex agent workflows.
SKILL.md
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