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Orchestrate

OrganizationPopular
cursor
orchestrate

Use only when the user explicitly types `/orchestrate <goal>` to decompose a large task, spawn a tree of parallel cloud-agent workers/subplanners/verifiers via the Cursor SDK, and collect structured handoffs; do not invoke autonomously.

Overview

Publishercursor
Repositoryplugins
Skill nameorchestrate
Stars
8K
Forks
728
Bundled files
48
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.

  • 48 bundled files

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

  • Open source

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

Installation

Install the Orchestrate 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/cursor/plugins.git /tmp/plugins
mkdir -p .claude/skills
cp -r /tmp/plugins/orchestrate/skills/orchestrate .claude/skills/orchestrate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Orchestrate 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 Orchestrate 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 Orchestrate 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.

Orchestrate

An explicit /orchestrate <goal> fans out a large task across parallel Cursor cloud agents. Workers don't talk to each other; they talk up through structured handoffs. The spawn, wait, and handoff loop lives in scripts/cli.ts. The planner writes plan.json, the script executes it, and the planner reads handoffs to decide what comes next. Long-running agent loops drift; a script with a JSON state file keeps its footing.

Required reading: the cursor-sdk skill (cursor/plugins/cursor-sdk). Spawning, auth, and the error taxonomy live there. Don't reimplement what that skill already documents.

Setup

  • CURSOR_API_KEY must be a personal/user key. Create it from Cursor Dashboard > Integrations, then read cursor-sdk Auth before using it.
  • SLACK_BOT_TOKEN is optional. When set, pass --slack-channel <id> to kickoff or the first run --root, or set SLACK_CHANNEL_ID. The script stores the channel in plan.slackChannel, posts the kickoff thread there, mirrors task status, and reads Andon reactions. When the token is unset, the script logs once and runs without Slack visibility; correctness does not change.

Core principles

These rules make the tree self-converging without global coordination.

  1. Planners own scopes and publish tasks. They do no coding. Writing plan.json, reading handoffs, and deciding what's next are planner work. Editing files, running git merge, and fixing conflicts inline are not. If a planner feels the urge to code, it publishes a task for a worker instead.
  2. Planners don't know who picks up their tasks. The script routes each task to a cloud agent. The planner's mental model stays at the task level.
  3. Workers are isolated. One task, one clone of the repo, no channel to any other agent. One handoff when done.
  4. Subplanners are recursive planners. A planner publishes a "subplan this slice" task; the subplanner fully owns that slice and hands back an aggregated handoff.
  5. Continuous motion via handoffs. A planner that thought it was done can receive a late handoff and replan. No "finished" state until the planner decides to stop publishing.
  6. Propagation, not synchronization. No cross-talk between siblings. No shared state between levels. Each level sees only its children's handoffs.

Node types

NodeRuns the loop?ScopeOutput
PlanneryesEntire user goalUser-facing message + optional PR
Subplanner (↻)yesOne slice of parent's scopeHandoff to parent
WorkernoOne concrete taskHandoff to spawning planner
VerifiernoOne target's acceptance criteriaVerdict handoff to spawning planner
Gitn/aShared mediumBranches (code) + handoffs/ (meaning)

Role

Two roles, one skill. Read your role's reference file and skip the other.

Dispatcher. You're in a local IDE session and the user typed /orchestrate <goal>. Your job is to kick off a cloud root planner and return its URL. See references/dispatcher.md. One-shot; you are not the planner.

Planner (root or sub). You were spawned with a structured prompt that opens with "You are the root planner for:" or "You are a subplanner for:". Or the user chose to run the planning loop locally. You own a scope, publish tasks, read handoffs, decide what's next. See references/planner.md.

disable-model-invocation: true means this skill loads only on explicit invocation.

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 Orchestrate AI skill do?

Use only when the user explicitly types `/orchestrate <goal>` to decompose a large task, spawn a tree of parallel cloud-agent workers/subplanners/verifiers via the Cursor SDK, and collect structured handoffs; do not invoke autonomously.

Why use Orchestrate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cursor/plugins/tree/main/orchestrate/skills/orchestrate. 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 Orchestrate?

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 Orchestrate?

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

Is the Orchestrate AI skill free?

It is published on GitHub by cursor. 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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