Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add openma-ai/open-managed-agents --skill deploy-openmagit clone --depth 1 https://github.com/openma-ai/open-managed-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/openma-ai/open-managed-agents/deploy-openma)<a href="https://agentmods.dev/skills/openma-ai/open-managed-agents/deploy-openma"><img src="https://agentmods.dev/badge/skills/openma-ai/open-managed-agents/deploy-openma/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openma-ai/open-managed-agents/deploy-openma"><img src="https://agentmods.dev/badge/skills/openma-ai/open-managed-agents/deploy-openma.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.01125 |
| Opus 5.5 | $0.00032 | $0.00450 |
| Sonnet 5.5 | $0.00016 | $0.00225 |
| Haiku 4.5 | $0.00008 | $0.00112 |
Grade A, and why
deploy-openma scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 104 lines · 80 tokens per session scan A 99cb612f3d92
deploy-openma is a skill published in the GitHub repository openma-ai/open-managed-agents (323 stars, last pushed yesterday), licensed Apache-2.0. It adds 80 tokens to every session and 1,125 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-17.
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