A comprehensive interface for managing Viam robotics infrastructure, from initial CLI installation to advanced fleet and data pipeline automation.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install viam-cli
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 viam-cli using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Viam CLI Manager is a powerful tool designed to streamline the lifecycle of robotics development within the Openclaw Skills ecosystem. It provides a unified interface for developers to interact with the Viam platform, handling everything from local environment setup and authentication to complex remote operations on robot fleets. By integrating with Openclaw Skills, this tool enables AI agents to automate machine configuration, module deployment, and data management tasks with high precision.
This skill emphasizes security by leveraging environment variables for authentication instead of hardcoded credentials and follows a strict read-first approach to ensure safe infrastructure management. Whether you are scaffolding new modules, debugging remote machines via WebRTC tunnels, or managing cloud-based ML datasets, this skill simplifies the developer experience across the entire Viam surface.
To get started with this skill, ensure you have Homebrew installed. The skill will automatically detect if the viam CLI is missing and prompt for installation. Use the following commands for manual setup:
# Add the Viam tap and install the CLI
brew tap viamrobotics/brews
brew install viam
# Verify the installation
viam version
# Authenticate the session
viam login
The Viam CLI Manager interacts with various platform entities and organizes data as follows:
| Entity | Description |
|---|---|
| Profiles | Local CLI configuration and authentication contexts for different environments. |
| Machines | Metadata including IDs, parts, and real-time logs for robotics hardware. |
| Modules | Scaffolding code, registry metadata, and versioning for custom components. |
| Datasets | Collections of tabular and binary (image) data used for ML training jobs. |
| Pipelines | Logic for data transformations, precompute jobs, and cloud synchronization. |
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