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Managed Agent

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ruvnet
managed-agent

Run an Anthropic Claude Managed Agent — a cloud agent harness (container + filesystem + tools), the cloud counterpart of the local wasm-agent runtime

Overview

Publisherruvnet
Repositoryruflo
Skill namemanaged-agent
Stars
72.7K
Forks
8.6K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Managed Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/plugins/ruflo-agent/skills/managed-agent .claude/skills/managed-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Managed Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Managed Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Managed Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Managed Agent (Anthropic cloud runtime)

ruflo-agent has two agent runtimes behind one mental model:

RuntimeToolsUse it when
WASM (local, rvagent)wasm_agent_* / wasm_gallery_*fast, free, ephemeral, offline, untrusted code in a sandbox
Managed (Anthropic cloud)managed_agent_* (this skill)long-running / async work (minutes–hours), a real cloud container with pre-installed packages + network, persistent filesystem + transcript across turns

This skill drives the managed runtime — Anthropic's Claude Managed Agents (beta). The model: Agent (model + system + tools + MCP servers + skills) → Environment (container template) → Session (running instance) → Events (turns / tool-use / status, persisted server-side). See docs/adr/0001-wasm-contract.md and project ADR-115.

Prerequisites

  • ANTHROPIC_API_KEY (or CLAUDE_API_KEY) in the environment, with Claude Managed Agents beta access.
  • If absent, every managed_agent_* tool returns a structured "use wasm_agent_create for a local no-key runtime" error — fall back to the WASM skill.

Steps

  1. Createmcp__plugin_ruflo-core_ruflo__managed_agent_create { model?, system?, name?, networking?, packages?, initScript?, mcpServers?, skills? }{ sessionId, agentId, environmentId, status }. Provisions Agent + Environment + Session. Save the three ids.

    • mcpServers: [{type:"url", url, name, authorization_token?}] — the cloud agent must be able to reach the URL. A local ruflo mcp start is not reachable from Anthropic's cloud; deploy/tunnel an HTTP ruflo MCP server first if you want the cloud agent to have ruflo's tools.
    • packages: {pip?:[], npm?:[], apt?:[], cargo?:[], gem?:[], go?:[]} — installed in the container.
  2. Promptmcp__plugin_ruflo-core_ruflo__managed_agent_prompt { sessionId, message, maxWaitMs? } → sends a user turn, polls the event log until the session goes idle (default 180s, capped 600s) → { finished, status, stopReason, assistantText, toolUses[], eventCount }. For very long tasks, raise maxWaitMs or follow up with managed_agent_events.

  3. Inspectmcp__plugin_ruflo-core_ruflo__managed_agent_status { sessionId } (idle/running/error) · mcp__plugin_ruflo-core_ruflo__managed_agent_events { sessionId, raw? } (full transcript: user turns, agent thinking, tool_use, tool_result, status — the cloud counterpart of wasm_agent_files).

  4. Listmcp__plugin_ruflo-core_ruflo__managed_agent_list { limit? } — every session on the org (so you can see which are still running / billing).

  5. Terminatemcp__plugin_ruflo-core_ruflo__managed_agent_terminate { sessionId, environmentId? }always do this when done: a cloud session keeps billing container time + tokens until deleted. Pass environmentId to also delete the environment ruflo created.

Cost & safety

  • Managed Agents bill per session (LM tokens + container time) and are rate-limited per org. Estimate before a long run; record completed sessions to the cost-tracking namespace.
  • Treat orphaned sessions like leaked resources — managed_agent_list then managed_agent_terminate anything stale.
  • Beta API (managed-agents-2026-04-01); multiagent / define-outcomes on the agent config are research preview.

Quick example

managed_agent_create  { "model": "claude-haiku-4-5-20251001", "system": "Terse. Do exactly what is asked.", "name": "scratch" }
  → { sessionId: "sesn_…", agentId: "agent_…", environmentId: "env_…", status: "idle" }
managed_agent_prompt  { "sessionId": "sesn_…", "message": "echo hello > /tmp/x && cat /tmp/x — then stop." , "maxWaitMs": 60000 }
  → { finished: true, status: "idle", stopReason: "end_turn", assistantText: "Done.", toolUses: [{name:"bash", input:{command:"echo hello > /tmp/x && cat /tmp/x"}}] }
managed_agent_terminate { "sessionId": "sesn_…", "environmentId": "env_…" }
  → { sessionDeleted: true, environmentDeleted: true }

Frequently asked questions

What does the Managed Agent AI skill do?

Run an Anthropic Claude Managed Agent — a cloud agent harness (container + filesystem + tools), the cloud counterpart of the local wasm-agent runtime

Why use Managed Agent on TypingMind?

Because you install it once and use it with any model. Managed Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Managed Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agent/skills/managed-agent. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Managed Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Managed Agent?

As many as you like. As long as a model supports skills, you can use Managed Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Managed Agent AI skill free?

Yes. It is published on GitHub by ruvnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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