orchestrate
Coordinates several smaller agents to perform complex tasks. Ensures each works on its part without conflicts and integrates the results.
What it does
This tool helps break down large, complex tasks into smaller parts, which can be worked on by different AI 'helpers' simultaneously. It then combines the results and ensures everything works well together.
When to use it
- ✓When a task has multiple parts that can be done independently (like research, review, or coding).
- ✓When doing multiple parts at the same time can make the work faster or better.
- ✓When you need to combine several different pieces of information or results into one.
Example of what to ask your AI
“Break down the creation of a website into design research, content writing, and development. Then, combine everything into a functional website.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool divides the big task into smaller pieces and assigns each piece to a different AI 'helper'. It makes sure the helpers don't do the same thing and then combines all their answers to deliver the final result.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill orchestrateOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/orchestrate/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Orchestrate
Coordinate substantial work across focused subagents while remaining available to the user and retaining responsibility for the integrated result.
When to Use
- Use when a task has multiple independent research, review, or implementation lanes.
- Use when parallel work will materially reduce elapsed time or improve coverage.
- Use when a coordinator must synthesize several bounded outputs into one verified result.
Keep trivial tasks with the coordinator.
Workflow
- Decompose the task into distinct, bounded assignments with explicit outputs.
- Run narrow, read-only scouts in parallel with low reasoning effort and no inherited conversation when the runtime supports those controls. Give each scout all scoped context and evidence required to complete its assignment.
- Use medium reasoning effort for routine implementation and high reasoning effort for difficult work.
- Give each subagent distinct ownership. Prevent overlapping assignments, and instruct leaf workers not to delegate.
- Integrate the outputs, resolve conflicts, and verify the combined result.
- Keep approvals and externally consequential decisions with the user.
Examples
- For a repository-wide feature, assign non-overlapping agents to architecture inspection, implementation, and test review, then integrate their findings and run the final verification from the coordinator.
- For a research brief, assign independent sources or questions to read-only scouts, reconcile disagreements, and keep the final judgment with the coordinator.
Limitations
- Requires a runtime that exposes subagent or delegation tools; otherwise keep the work with the coordinator.
- Delegation does not authorize file mutations, public actions, purchases, or other consequential operations beyond the user's original scope.
- Parallel agents can add cost and coordination overhead, so use them only when the task is substantial enough to benefit.
- The coordinator remains responsible for checking claims, changes, tests, and the final answer.
FAQ
What are 'subagents'?
They are like smaller AI 'helpers' that the main tool uses to complete specific parts of the task.
Why not use it for small tasks?
For small tasks, it's faster and simpler to do everything at once. Using multiple 'helpers' for something small can create more work than it saves.
Who is responsible for the final result?
The main tool (the 'coordinator') is always responsible for checking if all parts are correct and if the final result is good.
Details
Original description by the author
Coordinate focused subagents on substantial work, keep their ownership non-overlapping, and integrate verified results. Use for large-scope Codex tasks; keep trivial work with the coordinator.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3776
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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