Working Across Computers logo

Working Across Computers

OrganizationPopular
letta-ai
working-across-computers

Guides work across Cloud, local, and other available computers, including teleporting conversations, running remote subagents, and transferring files. Load when a request needs the user's laptop or another machine's files, apps, or tools; when connecting a computer; or when moving or delegating work between local and Cloud in either direction, including running Cloud subagents from local.

Overview

Publisherletta-ai
Repositoryletta-code
Skill nameworking-across-computers
Stars
3.4K
Forks
411
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by letta-ai on GitHub. Read the source before you install it.

Installation

Install the Working Across Computers 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/letta-ai/letta-code.git /tmp/letta-code
mkdir -p .claude/skills
cp -r /tmp/letta-code/src/skills/builtin/working-across-computers .claude/skills/working-across-computers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Working Across Computers 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 Working Across Computers 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 Working Across Computers 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.

Working Across Computers

Discover access before declaring a limitation

The current filesystem is not the full set of computers you can use. When a request needs another computer, run letta teleport list. If a suitable computer is available, delegate there; otherwise follow the connection instructions. Do not infer that another computer is inaccessible merely because you are running in Cloud.

Choose where to work

  • When running in Cloud, prefer staying there and delegating local work to subagents on the relevant computer.
  • If the user explicitly asks to move this conversation locally or elsewhere, teleport it.
  • Moving a conversation that started locally into Cloud is a normal workflow: transfer needed files first, then teleport.
IntentMechanism
Run work elsewhere while staying hereAgent
Bring a remote file into a Cloud conversationRemote agent: sandbox upload
Put a Cloud file onto another computerRemote agent: sandbox download
Continue this conversation elsewhereteleport

Conversation history and agent memory follow the conversation. Files, working directories, installed tools, credentials, and running services belong to each computer; teleporting does not copy the workspace.

For remote subagents, set computer and pass source paths and destination conversation IDs explicitly. Use the Agent tool definition for invocation and resume options.

If the user wants to connect a new computer, letta teleport list has no suitable target, or the requested local machine is missing or unreachable, read Connect a computer for remote access.

Move this conversation

Run the teleport handoff alone as the final tool call. After success, do not poll or run more source-side tools; the same conversation resumes at the destination automatically. Set the working directory and check required setup there. If teleport fails, stay on the source and resolve the error before retrying. The Cloud sandbox remains available while you work elsewhere.

Local → Cloud

Upload any files needed in Cloud:

bash
letta sandbox upload <local-path>

Keep the returned paths, repository/branch, and next action in context, then create or resume this conversation's Cloud sandbox:

bash
letta teleport cloud

Cloud → other computers

List available computers:

bash
letta teleport list

Have a remote subagent download any needed files and verify completion before moving. Use a returned computer name, device ID, or connection ID:

bash
letta teleport <computer>

With exactly one online Desktop, open with Remote Access enabled, you can use the shortcut:

bash
letta teleport local

Transfer files to or from Cloud

Run upload where the local file exists; run download where the local copy should be saved.

bash
# Use the executing conversation's Cloud sandbox
letta sandbox upload <local-path>
letta sandbox download <sandbox-path> --to <local-path>

# Explicit Cloud conversation (sandboxes are per-conversation)
letta sandbox upload <local-path> --conversation <destination-conversation-id>
letta sandbox download <sandbox-path> \
  --conversation <source-conversation-id> --to <local-path>

# An agent's main/default conversation
letta sandbox upload <local-path> --agent <agent-id>
letta sandbox download <sandbox-path> --agent <agent-id> --to <local-path>
  • --agent alone selects that agent's main/default conversation. Concrete --conversation IDs resolve their owning agent.
  • Target flags override the executing session without changing its identity. Do not replace the subagent's identity environment variables with the parent's.
  • The executing computer's credentials must authorize access to the target.
  • Commands return JSON. Use the exact upload path returned; never reconstruct it from the filename.
  • Download sources must be under /root/downloads; --to selects the destination path on the receiving computer. Without --to, download saves under the source file's basename.
  • Transfer files directly, not as base64 or file contents through model messages. Return the path and destination IDs; verify received contents or checksums.
  • These commands transfer files to/from Cloud sandboxes, not arbitrary remote filesystems. No sandbox ID or separate wake command is needed.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Working Across Computers AI skill do?

Guides work across Cloud, local, and other available computers, including teleporting conversations, running remote subagents, and transferring files. Load when a request needs the user's laptop or another machine's files, apps, or tools; when connecting a computer; or when moving or delegating work between local and Cloud in either direction, including running Cloud subagents from local.

Why use Working Across Computers on TypingMind?

Because you install it once and use it with any model. Working Across Computers 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 Working Across Computers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/letta-ai/letta-code/tree/main/src/skills/builtin/working-across-computers. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Working Across Computers?

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 Working Across Computers?

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

Is the Working Across Computers AI skill free?

Yes. It is published on GitHub by letta-ai under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇