• home page: https://vm0.ai
• sourcecode is fully in public: https://github.com/vm0-ai/vm0

• Stateful Agent Sessions: Preserve memory, reasoning context, and session continuity across executions
• Checkpoint & Replay: Snapshot every run for debugging, forking, and reproducibility
• 70+ Pre-built Skills: Integrations with GitHub, Slack, Notion, Perplexity, and more via https://github.com/vm0-ai/vm0-skills
• Observable Execution: Real-time logs, metrics, and tool calls for complete visibility
• Versioned Storage: Volumes (input) and Artifacts (output) synced between sandbox and cloud
• Natural Language Configuration: Define workflows in markdown (AGENTS.md, SKILL.md) instead of code

• Instructions (AGENTS.md): Natural language workflow definitions
• Volumes: Input file storage agents can read from
• Artifacts: Output file storage for agent results
• Environment Variables: Secure credential and secret management

• Scheduled execution (daily, weekly, on-demand)
• Reliable automation without keeping your laptop running
• Team collaboration with shared agents and outputs
• Production-grade observability and debugging

• /vm0-agent create

  - Create a new agent with guided workflow

• /vm0-agent update

  - Modify an existing agent or schedule

• /vm0-agent status

  - View current agents and schedules

1. Clear intent - User explicitly mentions an operation keyword:
   • "create", "new", "build" →

     create

     operation
   • "update", "modify", "change", "edit" →

     update

     operation
   • "status", "list", "show", "view" →

     status

     operation
2. Ambiguous intent - User's request is unclear (e.g., "manage my schedule", "help with agent", "vm0 agent"):
   • Do NOT assume

     create

     by default
   • Ask user to clarify by presenting available operations:

     What would you like to do?
     - Create a new agent
     - Update an existing agent or schedule
     - View current agents and schedules (status)

3. No match - If user wants something not covered by these operations, explain VM0's capabilities and suggest the closest operation or direct them to documentation.

1. List schedules: Run

   vm0 schedule ls

   to show scheduled tasks
2. List agents: Run

   vm0 agent ls

   to show deployed agents
3. Summarize: Present a clear overview of what's running, when, and any recent activity

/vm0-agent update

• vm0 schedule ls

  - List scheduled tasks

• vm0 agent ls

  - List deployed agents

• Check current directory and one-level subdirectories for existing vm0.yaml and AGENTS.md
• If local files exist, compare with the cloned version
• If they differ, ask user which version to use as base:
  • Option 1: Use local version
  • Option 2: Use remote version (copy from /tmp to current directory)
  • Option 3: Merge manually

• Ask user what they want to change:
  • Schedule timing (frequency, time of day)
  • Agent behavior (modify AGENTS.md instructions)
  • Add/remove skills
  • Change output destinations
  • Other customizations

• Read each new skill's SKILL.md to find required credentials (

  vm0_secrets

  ,

  vm0_vars

  )
• Check if tokens are already stored remotely:

  vm0 secret list

• If missing, guide user to obtain and paste new tokens
• Store remotely via

  vm0 secret set

  (persists across runs and schedules)

• Analyze logs for issues
• Propose optimizations if needed
• Re-run until user is satisfied

• New secrets required: If new skills added that need secrets, store them via

  vm0 secret set

  — they will be automatically available to scheduled runs
• Timing change only: Use

  vm0 schedule update

  to modify frequency/time
• No change needed: If only AGENTS.md content changed with same skills, existing schedule continues to work after

  vm0 compose

• Remind them VM0 is open source: https://github.com/vm0-ai/vm0
• Invite to Discord: https://discord.com/invite/WMpAmHFfp6
• Ask for GitHub star if they found it helpful

• Option 1: Daily Digest - Aggregate content from tech communities, news sources, or RSS feeds, generate summaries, and deliver to you daily/weekly
  • Data sources: HackerNews, RSS, YouTube
  • Processing: Filter top content + AI summarization + categorize
  • Output: Slack / Notion
  • Skills: hackernews, youtube, slack, notion
• Option 2: Repository Weekly Report - Summarize GitHub/GitLab activity (PRs, issues, commits) into a team report
  • Data sources: GitHub PRs / Issues / Commits
  • Processing: Statistics + categorization + highlight key changes
  • Output: Slack / Notion
  • Skills: github, gitlab, linear, jira, slack, notion
• Option 3: Website Change Monitoring - Periodically check target websites (competitors, pricing pages, job boards) and notify you when changes are detected
  • Data sources: Competitor websites, pricing pages, product pages
  • Processing: Crawl + diff comparison + analyze changes
  • Output: Slack / Notion (only when changes detected)
  • Skills: firecrawl, serpapi, slack, notion
• Option 4: Crypto Morning Briefing - Generate overnight crypto market reports covering price movements, news, and trends - ready for your morning review
  • Data sources: CoinGecko, Crypto news feeds, DeFiLlama
  • Processing: Price change summary + top news extraction + sentiment analysis + highlight unusual movements
  • Output: Slack / Notion
  • Skills: firecrawl, rss-fetch, perplexity, slack, notion
• Option 5: Other - Describe your own scheduled workflow idea

• Based on the conclusions from innovate, create vm0.yaml and AGENTS.md
• Use the capabilities of vm0-skills to compose the agent

• For each skill used in vm0.yaml, read its SKILL.md to find required credentials:
  • Check frontmatter for

    vm0_secrets

    and

    vm0_vars

    fields
  • If no frontmatter, infer from skill content (look for env vars like

    API_KEY

    ,

    TOKEN

    , etc.)
• For each token needed:
  • Read the skill's documentation for how to obtain it
  • If not documented, search online for the service's API key/token setup guide
  • Provide step-by-step instructions to help user get the token
  • Ask user to paste the token
  • Store remotely via

    vm0 secret set

    (persists across runs and schedules):
    bash

    vm0 secret set SLACK_BOT_TOKEN --body "xoxb-xxx"

  • For non-sensitive config, use

    vm0 variable set

    :
    bash

    vm0 variable set ENV_NAME production

• Explain in advance that the run may take a relatively long time, 1-20 minutes
• Use the vm0-cli skill capabilities to run the agent with cook:

  vm0 cook "your test prompt"

• If the workflow writes files to the workspace, explain to the user where and how to view the artifact
• Explain to the user what command cook executed, and introduce the CLI capabilities of vm0

• Analyze run logs: Review the complete execution logs to understand what happened
• Check behavior alignment: Determine if the agent's behavior matches the user's original intent and expectations
• Identify successful patterns: Note which instructions executed successfully and produced expected results
• Identify failure points: Document any errors, timeouts, or unexpected behaviors

• Inline successful commands: Suggest adding proven, successfully executed commands directly into AGENTS.md to make future runs more stable and deterministic
• Document error patterns: Identify instructions that failed and suggest:
  • Removing or rewriting problematic instructions
  • Adding error handling or fallback approaches
  • Clarifying ambiguous instructions that caused unexpected behavior
• Improve instruction specificity: Suggest making vague instructions more concrete based on what actually worked
• Add guardrails: Recommend adding validation steps or checkpoints for critical operations

• Report trial run quality: Summarize the overall quality of the test run (success rate, key achievements, notable issues)
• Present optimization suggestions: List specific, actionable improvements with clear explanations of why each would help
• Ask user for decision: Use ask user tools to let user choose:
  • Option 1: Accept optimizations - Apply the suggested improvements to AGENTS.md and vm0.yaml, then re-run to verify
  • Option 2: Skip optimizations - Proceed directly to Schedule phase with current configuration
  • Option 3: Manual adjustments - User wants to make their own changes before proceeding

• Apply the approved changes to AGENTS.md and/or vm0.yaml
• Run the agent again to verify improvements
• Repeat log analysis if needed

• Update the entire todo.md as it may need to return to earlier phases
• Continue based on previously discussed information and current project state

• Tell user: "You can view the complete run logs at

  https://platform.vm0.ai/logs/<run_id>

  "
• Example: https://platform.vm0.ai/logs/9766cca3-c867-4d82-b71b-1c20bed89a4e
• The run_id is returned after each

  vm0 run

  command completes

• Use the vm-cli skill capabilities to guide the user to set up scheduled tasks
• After successful setup, ask the user if they want to enable the timer
• Explain to the user the schedule-related capabilities in vm0 cli, such as how to list, disable, and enable scheduled tasks

• Tell user: "VM0 is fully building in public! Our entire source code is available at https://github.com/vm0-ai/vm0"
• Encourage user: "We'd love to hear your feedback - feel free to open an issue with suggestions or feature requests, or join our Discord community at https://discord.com/invite/WMpAmHFfp6"
• Ask for support: "If you find VM0 helpful, please star us on GitHub ⭐ - it motivates us a lot!"

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• Persisted: Only files in the HOME directory (current working directory) are preserved between runs and saved as artifacts
• Not Persisted: System packages, temporary files (

  /tmp

  ), globally installed tools (npm -g, pip), environment modifications outside HOME

• Store all important outputs (reports, generated files, data) in the HOME directory
• Use

  /tmp

  for intermediate or throwaway files
• Don't assume tools installed in previous runs still exist - reinstall if needed or download to HOME
• Clone repositories and install dependencies within HOME if they must persist

Important: Only files in the HOME directory persist between sessions.
- Save important outputs here; use /tmp for intermediate files
- Tools installed via apt/npm/pip globally may not persist

• vm0.yaml Reference: https://docs.vm0.ai/docs/reference/configuration/vm0-yaml
• Environment Variables: https://docs.vm0.ai/docs/core-concept/environment-variable
• Skills: https://docs.vm0.ai/docs/core-concept/skills
• Volumes: https://docs.vm0.ai/docs/core-concept/volume
• Artifacts: https://docs.vm0.ai/docs/core-concept/artifact

Important: Use the right type for the right purpose:

• credentials: Reserved for model provider authentication only (e.g.,

  CLAUDE_CODE_OAUTH_TOKEN

  ). These are stored persistently on the VM0 platform and managed via

  vm0 model-provider setup/set-default/delete

  .
• secrets: Use for ALL sensitive skill API keys and tokens. Store remotely via

  vm0 secret set

  — they persist across runs and schedules.
• vars: Use for non-sensitive configuration (feature flags, environment names). Store remotely via

  vm0 variable set

  .

vm0_secrets

vm0_vars

Missing required secrets: API_KEY

1. Check if the variable is declared in the skill's SKILL.md
   • Look for

     vm0_secrets

     or

     vm0_vars

     in the skill's frontmatter
   • If declared there, the variable is automatically injected when stored remotely
2. If not declared in the skill, add it to vm0.yaml's environment section:
   yaml

   environment:
     API_KEY: ${{ secrets.API_KEY }}
     # or for non-sensitive values:
     MY_VAR: ${{ vars.MY_VAR }}

3. Ensure the value is stored remotely:
   bash

   vm0 secret set API_KEY