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 agentmods add skills/agno-agi/agentos-docker/setup-platformnpx skills add agno-agi/agentos-docker --skill setup-platformgit clone --depth 1 https://github.com/agno-agi/agentos-dockerWhat 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 | $0.00067 | $0.01495 |
| Opus 5 | $0.00034 | $0.00747 |
| Sonnet 5 | $0.00013 | $0.00299 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
setup-platform 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 2d ago.
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- setup-platform — 100% identical, 0 lines differ
- setup-platform — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set Up the Platform
Coding-agent workflow: run as
/setup-platformor by describing the task.
Take the user from a fresh clone to a running platform with their first agent live on it. Step 6 is the point of the trip; everything before it is setup, everything after is hand-over.
Be self-driving: do anything you can do yourself (open a file, open a URL, launch an app). Stop only for what needs a human: a secret, an install, a sign-in. Never print or echo secret values.
Narrate: open with this map (tune the words, keep the shape), then a line per step.
Kicking off /setup-platform. Here's the map:
1. Docker — confirm it's installed and running
2. Environment — .env and your OpenAI key
3. Boot — build and start the platform containers
4. Prove it — a real agent answer over the MCP endpoint
5. Connect the UI — os.agno.com, one click
6. First agent — we build it together, live
7. Make it yours — your platform in its own private repo
8. The loop — the skills you own from here
0. Read the manual
Read AGENTS.md end to end.
1. Docker
docker info must succeed. Installed but not running → start it (open -a Docker on macOS) and poll. Not installed → stop and give install steps.
2. Environment
cp example.env .env, then set OPENAI_API_KEY:
- Already in their shell → say so and copy it across without reading or printing it.
- Otherwise open
.envin their editor (cursor,code) and ask them to paste it. Never open a terminal editor from your shell — it hangs the session.
3. Boot
docker compose up -d --build, then poll http://localhost:8000/docs until 200 (first build takes minutes). If it never comes up, read docker compose logs agentos-api.
4. Prove it
./scripts/mcp_check.sh prints "MCP OK" and a real agent answer. Quote the answer — it's their Platform Manager — and say the MCP server is live.
5. Connect the AgentOS UI
Render the connection as a table, then one line of direction:
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.
- 2d ago First seen · 110 lines · 67 tokens per session scan A 8e8a0fa1e8e5
setup-platform is a skill published in the GitHub repository agno-agi/agentos-docker (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,495 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
Other skills, from other repositories
setup-platform
Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.
setup-platform
Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.
deploy-platform
Deploy this AgentOS to production with this template's deploy scripts — preflight the provider CLI and account, run the up.sh script, complete the JWT key step, verify the live platform on its public URL, then hand over the redeploy/logs/teardown instructions. Use this skill when the user asks to deploy, ship to…
improve-agent
Autonomous hardening loop for an existing agent — derive probes from the agent's INSTRUCTIONS and from its real usage recorded in the database, run them against the live container, judge responses, edit the agent file, and re-probe until it reliably does what its instructions say. No user input needed. Use to harden…
deploy-platform
Deploy this AgentOS to production with this template's deploy scripts — preflight the provider CLI and account, run the up.sh script, complete the JWT key step, verify the live platform on its public URL, then hand over the redeploy/logs/teardown instructions. Use this skill when the user asks to deploy, ship to…
improve-agent
Autonomous hardening loop for an existing agent — derive probes from the agent's INSTRUCTIONS and from its real usage recorded in the database, run them against the live container, judge responses, edit the agent file, and re-probe until it reliably does what its instructions say. No user input needed. Use to harden…