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Paseo

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getpaseo
paseo

Paseo reference for managing projects, workspaces, workspace scripts, agents, schedules, and heartbeats.

Overview

Publishergetpaseo
Repositorypaseo
Skill namepaseo
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19.1K
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2.2K
Bundled files
Instructions only
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 getpaseo on GitHub. Read the source before you install it.

Installation

Install the Paseo 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.
    https://github.com/getpaseo/paseo/tree/main/skills/paseo
  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/getpaseo/paseo.git /tmp/paseo
mkdir -p .claude/skills
cp -r /tmp/paseo/skills/paseo .claude/skills/paseo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paseo 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 Paseo 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 Paseo 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.

Paseo is a remote daemon that manages coding agents, terminals. Control it through MCP tools or the CLI.

Projects

Manage the daemon's project registry through the CLI:

bash
paseo project create [path]
paseo project ls
paseo project rename <project-id> <name>
paseo project rename <project-id> --reset
paseo project delete <project-id>

For a local daemon, project create defaults to the current directory and resolves relative paths on the CLI machine. With --host or PASEO_HOST, always provide a path; the target daemon interprets it on its own machine. Deleting a project archives its active workspaces and removes the project from Paseo without deleting the project directory.

Workspaces

create_workspace — create a workspace independently of any agent. Required: isolation (local or worktree). Worktree isolation supports mode: "branch-off" | "checkout-branch" | "checkout-pr": use branchName/baseBranch for a new branch, branch for an existing branch, or prNumber plus optional forge/projectPath for a change request. worktreeSlug controls the managed path. Returns the workspace descriptor centered on workspaceId.

Choose baseBranch explicitly: origin/main selects the remote-tracking branch; refs/heads/main selects local main. Bare main prefers local main when it exists, otherwise origin/main. Paseo retains the resolved ref for workspace comparisons, even after rebasing the branch or changing its PR target.

list_workspaces — list active workspaces.

archive_workspace — { workspaceId }. Archives the workspace, its agents, and its terminals. Local directories remain; Paseo removes an owned worktree only after its final active workspace reference is archived.

rename_workspace — { workspaceId, name }. Rename workspace.

Workspace scripts

Configured paseo.json scripts use the same supervised lifecycle from tools and the CLI.

list_workspace_scripts — { workspaceId }. Lists configured scripts with lifecycle, service port, proxy URLs, health, exit code, and terminal ID.

start_workspace_script — { workspaceId, scriptName }. Starts one configured script through Paseo's managed workspace-script launcher and returns its status metadata.

stop_workspace_script — { workspaceId, scriptName }. Stops a running script through its supervised terminal and returns the stopped status metadata.

The matching CLI surface accepts either an explicit workspace ID or resolves the current directory:

bash
paseo script ls [--cwd <path> | --workspace <workspace-id>]
paseo script start <name> [--cwd <path> | --workspace <workspace-id>]
paseo script stop <name> [--cwd <path> | --workspace <workspace-id>]

Agents

create_agent — required: title, provider (claude/opus, codex/gpt-5.4, …), initialPrompt. Optional: workspaceId, notifyOnFinish, settings, labels. Returns { agentId, workspaceId, … }.

Initial runtime settings live under settings: modeId, thinkingOptionId, and provider-specific features. Agent profiles are the preferred source for these values. For Codex fast mode, pass settings: { features: { "fast_mode": true } } when creating the agent.

Agent-scoped creation always creates your subagent. Omit workspaceId to use your current workspace; pass a workspace returned by create_workspace for isolated delegation. Placement never changes parentage.

Detach is an explicit user action in the subagents track, not an agent tool. A cross-workspace child remains your subagent even though it also appears as a normal tab in its workspace.

Agent-scoped create_agent defaults notifyOnFinish to true. Set it to false only for truly fire-and-forget agents.

send_agent_prompt — { agentId, prompt }. Use for follow-ups to an existing agent. Agent-scoped prompt calls default to background: true and notifyOnFinish: true; top-level calls default to blocking with no callback. For a synchronous follow-up, pass background: false and use the returned result.

update_agent — { agentId, name?, labels?, settings? }. Use settings for runtime changes on an existing agent: modeId, model, thinkingOptionId, and provider-specific features. For Codex fast mode, pass settings: { features: { "fast_mode": true } }.

list_agents — filter by cwd, statuses, sinceHours, includeArchived.

archive_agent — { agentId }. Interrupts if running, removes from active list.

Agent profiles and provider discovery

list_profiles — named launch bundles configured by the human. Before choosing how to launch a delegated agent, call this tool and read every profile's notes. Pick a named profile the user requested, or the profile whose notes best match the work.

There is no profile parameter on create_agent. Materialize the selected profile into the call:

  • combine provider and model as the provider/model value for create_agent.provider
  • copy modeId to settings.modeId
  • copy thinkingOptionId to settings.thinkingOptionId
  • copy featureValues to settings.features

Omit absent values. Do not remember a selected profile or infer drift later; a profile is only launch configuration.

If no profile fits, or no profiles are configured, use the provider discovery tools below rather than guessing. Tell the user when you fall back because no configured profile fits.

list_providers — compact provider availability and modes.

list_models — full model list for one provider. Use only when you need model IDs or thinking options; the list can be large.

inspect_provider — compact provider capability and feature inspection. Required: provider; pass cwd when you are not in an agent-scoped session. Optional: settings with draft model, modeId, thinkingOptionId, and features.

Only set feature IDs returned by inspect_provider. For Codex fast mode, look for fast_mode and pass settings: { features: { "fast_mode": true } } to create_agent or update_agent.

Schedules and heartbeats

create_schedule — starts a new agent on a cron cadence. Required: prompt, cron, provider. Optional: timezone, name, cwd, maxRuns, expiresIn. Use when the recurring work should live in fresh agents.

create_heartbeat — sends you a prompt on a cron cadence. Required: prompt, cron. Optional: timezone, name, maxRuns, expiresIn. Use for reminders, PR/build babysitting, and status checks that should return to this conversation.

delete_heartbeat stops it. MCP intentionally exposes no heartbeat update tool; delete and recreate when its task or cadence changes.

Schedules have the full list/inspect/update/pause/resume/run-once/log/delete surface. Heartbeats deliberately do not.

Waiting

Agents take time — 10–30+ minutes is routine. Favor asynchronous workflows.

For agent-scoped create_agent and background send_agent_prompt, leave notifyOnFinish omitted or set it to true unless the work is truly fire-and-forget. You will get notified when the target agent finishes, errors, or needs permission. Move on to other work. The notification arrives on its own.

Don't poll list_agents or get_agent_status to "check on" a running agent. The notification will tell you.

CLI semantics

The CLI and tools use the same ownership semantics even where their syntax differs:

bash
paseo workspace create --isolation worktree --mode branch-off --new-branch fix-x --base origin/main
paseo workspace create --isolation worktree --mode checkout-branch --branch existing-work
paseo workspace create --isolation worktree --mode checkout-pr --pr-number 42
paseo run --provider codex/gpt-5.4 --mode full-access --workspace <workspace-id> "<prompt>"
paseo run --provider codex/gpt-5.4 --mode full-access --new-workspace worktree --worktree-mode branch-off --new-branch fix-x --base origin/main "<prompt>"
paseo send <agent-id> "<follow-up>"
paseo ls
paseo schedule create --cron "*/15 * * * *" "ping main build"
paseo heartbeat create --cron "*/15 * * * *" "check the build"

Discover with paseo --help and paseo <cmd> --help.

For product questions, setup, logs, version problems, or troubleshooting, use the paseo-help skill.

Frequently asked questions

What does the Paseo AI skill do?

Paseo reference for managing projects, workspaces, workspace scripts, agents, schedules, and heartbeats.

Why use Paseo on TypingMind?

Because you install it once and use it with any model. Paseo 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 Paseo in TypingMind?

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

Which AI models can use Paseo?

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 Paseo?

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

Is the Paseo AI skill free?

It is published on GitHub by getpaseo. Check the repository for licensing terms. 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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