How can I debug a failed agent run?

You can use the 'vm0 logs' command with a specific run ID to view agent events, system logs, network activity, and performance metrics.

What is the VM0 CLI skill used for?

The VM0 CLI skill is used to build, deploy, and run AI agents in secure, isolated sandboxes, allowing for safe execution and persistent data management.

How do I authenticate my VM0 account?

You can use 'vm0 auth login' for interactive browser-based login or set the 'VM0_TOKEN' environment variable for non-interactive or CI/CD environments.

What is the difference between volumes and artifacts?

Volumes are used to store and sync input files that agents need to read, while artifacts store the output files created by agents during their execution.

Can I use this skill for automated deployments?

Yes, the skill supports a non-interactive mode specifically designed for CI/CD pipelines like GitHub Actions, using flags to skip confirmation prompts.

VM0 AI Agent Orchestrator

Name: VM0 AI Agent Orchestrator
Author: vm0-ai

byvm0-ai

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Deployment & DevOps

Builds and runs AI agents in secure sandboxed environments with automated deployment and volume management.

About

The VM0 CLI skill empowers Claude to manage the entire lifecycle of AI agents within secure, isolated sandboxes. It enables users to initialize agent projects, deploy configurations via vm0.yaml, and execute tasks with persistent storage through cloud-synced volumes and artifacts. By providing deep integration with the VM0 platform, it simplifies complex workflows like secret management, model provider configuration, and non-interactive CI/CD deployments, making it an essential tool for developers building robust, production-ready AI agent systems.

Key Features

Secure sandboxed execution for AI agents
Automated project initialization and deployment
Persistent volume and artifact management
Comprehensive logging and usage analytics
Non-interactive CI/CD pipeline support
24 GitHub stars

Use Cases

Managing large datasets for agents via cloud-synced volumes
Automating agent tasks within GitHub Actions or CI/CD pipelines
Deploying complex AI agents to secure production environments

About

Key Features

Secure sandboxed execution for AI agents
Automated project initialization and deployment
Persistent volume and artifact management
Comprehensive logging and usage analytics
Non-interactive CI/CD pipeline support
24 GitHub stars

Use Cases

Managing large datasets for agents via cloud-synced volumes
Automating agent tasks within GitHub Actions or CI/CD pipelines
Deploying complex AI agents to secure production environments