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/deploy-openma

Deploy and self-host the OpenMA platform on a local machine, server, or cloud. Use when users ask to install OpenMA, bring up its Console and API, configure a self-hosted deployment, or troubleshoot first-boot setup on Node/Docker, Cloudflare, Fly.io, or Vercel. Not for merely

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open-managed-agents
3243 skills
Install
$ npx -y skills add openma-ai/open-managed-agents --skill deploy-openma --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/deploy-openma

Context preview

The summary Claude sees to decide when to auto-load this skill.

Deploy and self-host the OpenMA platform on a local machine, server, or cloud. Use when users ask to install OpenMA, bring up its Console and API, configure a self-hosted deployment, or troubleshoot first-boot setup on Node/Docker, Cloudflare, Fly.io, or Vercel. Not for merely

SKILL.md

deploy-openma.SKILL.md
name: deploy-openma
description: >
  Deploy and self-host the OpenMA platform on a local machine, server, or cloud.
  Use when users ask to install OpenMA, bring up its Console and API, configure
  a self-hosted deployment, or troubleshoot first-boot setup on Node/Docker,
  Cloudflare, Fly.io, or Vercel. Not for merely creating an agent on an existing
  OpenMA instance.

Deploy OpenMA

Help the user reach a working OpenMA Console and a verified agent task. Do the setup work rather than handing back a list of environment variables. This skill is a deployment workflow for a coding agent, not an executable server installer.

Establish the target

Use the user's specified host, platform, directory and existing credentials. Inspect what is available before asking questions. If the destination is unknown, ask whether this is a local trial or a server/cloud deployment. For a local trial, prefer a single-host Node/Docker setup when Docker and a supported isolated sandbox are available. Do not assume Docker alone provides the agent sandbox.

Keep questions to missing decisions or access: destination, sandbox choice, provider login and credentials. Generate application secrets and choose ordinary single-host storage defaults yourself. Do not require users to choose a database, queue or object store unless their deployment needs it.

Before provisioning, state the target, data location, exposure and any paid services. Obtain approval for costs or public exposure not already authorized. Do not switch cloud accounts, deploy to production or replace an existing installation merely because it is accessible.

Use the actual release

Repository: https://github.com/openma-ai/open-managed-agents

Reuse a suitable checkout or clone into an agreed, unused directory. Preserve local changes. Record the release/ref and resolved commit; do not assume the installed skill's directory is a server checkout. Read the checkout's `AGENTS.md`, `.node-version`, `package.json` and the selected target's configuration.

Use a user-selected release, or an identified published release. For a source trial, disclose the selected commit. Do not invent a Docker image tag or assume every Git commit has a published image. Compare current entrypoints and config with documentation when they disagree; report build/configuration failures rather than bypassing checks or claiming success.

Read only the selected deployment reference:

  • **Node/Docker:** [single-host setup](references/node-docker.md).
  • **Cloudflare, Fly.io or Vercel:** [cloud setup](references/cloud.md).

Credentials and existing data

  • Generate missing application secrets locally, save them in a protected file

or provider secret store, and keep them stable across retries and restarts. Preserve existing `BETTER_AUTH_SECRET`, `PLATFORM_ROOT_SECRET`, databases and volumes. A lost encryption root is a recovery problem, not a reason to generate a replacement and declare the installation repaired.

  • Let users log in through provider/browser flows. Accept API keys from a secret

manager, protected environment or local file; do not request them in chat, put values in command arguments, or echo them in output. Disable shell tracing around secret handling. Redact logs and resolved deployment configuration.

  • Keep authentication and sandbox isolation enabled. Do not use `AUTH_DISABLED`,

test adapters, a host subprocess or unscoped credential access as a shortcut.

  • For an existing install, establish backup and rollback before migrations or

redeployment. Do not reset volumes, rotate keys or overwrite configuration to make a smoke test pass.

Verify what the user can do

1. **Service ready:** check build/deploy completion, service status, sanitized startup logs and `/health`. Confirm the selected data storage and listening address. An HTTP 200 alone is not full deployment success. 2. **Console usable:** check the login page and help the user create/sign into the intended workspace. Never claim an account was created without evidence. 3. **Agent execution:** configure or select a Model Card and ready environment; use the current Console or authenticated SDK/CLI to run a small task. Verify model output and a harmless sandbox operation, such as writing then reading a temporary file, through persisted session events. Do not send private data to a model for testing. Respect the approved model/sandbox spending scope. 4. **Persistence:** for a fresh local trial, verify the test session remains accessible after a controlled restart if authorized. On an existing service, schedule disruptive verification rather than restarting it without agreement.

If credentials, runtime support or approval are missing, finish safe preparation and report the exact remaining step. Distinguish **prepared**, **service ready**, **agent execution verified**, and **persistence verified**. Do not substitute a no-sandbox model response for sandbox verification.

Investigate a failed step using its actual error and logs. Retry only after an identified change; recheck cloud resources before provisioning again. If progress requires new access or a destructive action, stop that step and ask rather than looping or silently changing the deployment design.

Handoff

Return the Console URL, release/commit, target, storage and secret locations (without values), verification results, and the exact start/stop/log commands for the deployed configuration. Call out incomplete checks and backup needs. Keep the handoff short; the user should not have to repeat the setup you did.

Read more
Ships withopen-managed-agents

Any model. Any harness. Any sandbox. Any cloud. OpenMA handles sessions, execution and recovery, so you can build and run agents with any model, any harness, any sandbox, on any cloud.

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TypeScript
Language
Apache-2.0
License
1h ago
Last commit
6mo ago
Created
3h ago
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Repo: openma-ai/open-managed-agents

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