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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.

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集成工具DockerOpenAI

实测评分 未实测

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包含的文件(1)

  • SKILL.md7.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Set Up the Platform

Coding-agent workflow: run as /setup-platform or 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 — MCP handshake and tools; an optional agent answer
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

If .env does not exist, 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 .env in their editor (cursor, code) and ask them to paste it. Never open a terminal editor from your shell — it hangs the session.

3. Boot

For standard development, 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.

For sandbox development, follow the host API commands in the sandbox setup guide. Select docker_sandbox() in sandbox_provider() and edit REPOSITORY in app/sandbox.py for the target repository and branch, and keep the Uvicorn process running in a persistent terminal. The host API uses Docker directly. Check /health and /config; Company should list its four tables, OS Manager, and the optional coding executors. Never start two APIs on the same port. The pinned framework admits sandbox HTTP runs through the durable PostgreSQL queue. Configuration and API startup do not prove execution: verify an engineer HTTP run and Company Task acceptance/completion separately. Keep the Product repository Source aligned with REPOSITORY.url; never bypass a framework rejection. For this host path use MCP_CHECK_HOST=1 ./scripts/mcp_check.sh below.

4. Prove it

./scripts/mcp_check.sh initializes MCP and verifies the required tools without a model call. For an answer, use --prompt "<read-only prompt>"; that spends model calls. Report which check was actually run.

5. Connect the AgentOS UI

Render the connection as a table, then one line of direction:

SettingValue
AgentOS UIhttps://os.agno.com/?ref=modal-template
Connection typeLocal
Endpointhttp://localhost:8000
NameLocal AgentOS

Most users arrive from the Agno onboarding with Connect your OS still open ("Awaiting connection"): tell them to flip back and hit Connect OS. Otherwise: sign in, Connect OS, fill the form from the table.

Don't gate on the click and don't ask "connect or build first?" — bridge straight into Step 6 in the same message. This table is written before anything from Step 6.

6. Build their first agent

Below the connect direction, ask the one question that starts it — plain text, no structured choice control:

Now let's build your first agent. Do you have a product you'd like to build an agent for — or a product you use that you'd like an agent for? Give me its docs or website URL and I'll build an agent that answers questions about it from its own docs, ready to serve in your product, in claude.ai and ChatGPT, and over MCP.

Or if you have something else in mind — issue triage, release notes, your weekly update — say it in your own words and I'll build that instead.

Whatever they type is the first discovery answer for create-agent: a URL or product name takes its product-agent pattern (Step 3 there); anything else takes its normal path. Close the message with the first build move, never with "ready?".

The product brief you hand create-agent when they name a product — complete, so it builds without asking more: the product pattern (Step 3 there), first declare and register a dedicated product_knowledge in app/registry.py (none ships by default), then ingest with the framework KnowledgeManagementTools(knowledge=product_knowledge).aingest_url in the selected Compose or host runtime, initial page cap 50, knowledge=product_knowledge, learning=shared_learning, instructions you write that carry the grounding rules in create-agent's product pattern, no other tools; three smoke probes.

Follow create-agent through its smoke test, then land where the agent now lives, in the same breath as its first answer:

  • In the UI — Refresh puts it in the Agents list, and its base on the Knowledge page.
  • On OS Manager's roster — registering in app/main.py lets OS Manager run it by name ("Agno, ask the <Product> agent…" through the agno MCP tool) from the UI, Slack, or any MCP client.
  • Ready to serve — REST inside their product and MCP now; claude.ai and ChatGPT once deployed (Step 8).

Stop before create-agent's own closing — Steps 7 and 8 replace it. If they push back or want to stop, adapt and carry on.

7. Make it yours

origin still points at the public template. Offer a home of its own — a quick beat, not a gate:

git remote rename origin upstream    # the template stays connected for updates
git remote add origin <their-private-repo-url>
git push -u origin main

With gh signed in: gh repo create agent-platform --private --source=. --push after the rename. Otherwise point them at https://github.com/new (private) and run the add and push once they paste the URL. git pull upstream main brings template updates.

8. Hand over the loop

Short summary of what you built, then the loop they own — lead with whichever the smoke test suggested:

One line: coding agents connect with uvx agno connect; claude.ai / ChatGPT connect over OAuth once deployed — /deploy-platform does that.

If the trip went smoothly, one more: the Knowledge page and the shared notebook ship empty and are theirs to fill.

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同一仓库的技能

Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

中文简介正在准备中。当前显示原文说明。

agno-agi/agentos-modal32026年10月23日 更新

Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior pinned down as tests. For failing suites or exploratory simulation-driven hardening, use eval-and-improve instead.

中文简介正在准备中。当前显示原文说明。

agno-agi/agentos-modal32026年10月23日 更新

Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.

中文简介正在准备中。当前显示原文说明。

agno-agi/agentos-modal32026年10月23日 更新

Evaluate and improve an existing AgentOS agent or team. Repair failing eval cases, or explore behavior by generating simulated user scenarios from instructions and sanitized usage, running probes, inspecting evidence and iterating within a bounded budget. Use for failing evals, autonomous hardening or simulation-driven improvement. Use create-evals for coverage authoring alone and extend-agent for a requested new capability.

中文简介正在准备中。当前显示原文说明。

agno-agi/agentos-modal32026年10月23日 更新

User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug, verifying each change against the selected development runtime. Use whenever the user names a concrete change to an agent, or wants a new building block available to the platform. For autonomous hardening with no specific change in mind, use eval-and-improve.

中文简介正在准备中。当前显示原文说明。

agno-agi/agentos-modal32026年10月23日 更新

Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.

中文简介正在准备中。当前显示原文说明。

agno-agi/agentos-modal32026年10月23日 更新

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