Multi Agent Orchestration logo

Multi Agent Orchestration

Community
seb1n
multi-agent-orchestration

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

Overview

Publisherseb1n
Repositoryawesome-ai-agent-skills
Skill namemulti-agent-orchestration
Stars
188
Forks
35
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by seb1n on GitHub. Read the source before you install it.

Installation

Install the Multi Agent Orchestration 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/seb1n/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/agent-engineering/multi-agent-orchestration .claude/skills/multi-agent-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multi Agent Orchestration 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 Multi Agent Orchestration 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 Multi Agent Orchestration 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.

Multi-Agent Orchestration

Use multiple agents only when specialization or safe parallelism outweighs coordination cost.

Use when

  • Split a large objective into independent, verifiable workstreams.
  • Coordinate specialists that need distinct tools, permissions, or context.
  • Run worker-reviewer, planner-executor, map-reduce, or bounded debate patterns.
  • Diagnose duplicate work, conflicting edits, weak handoffs, or stalled dependencies.

Do not delegate a tightly coupled, small, or inherently sequential task merely to increase agent count.

Inputs

Collect the objective, completion criteria, task graph, available agents and tools, concurrency limits, shared files or systems, authority boundaries, deadlines, budget, and final decision owner. State assumptions and unresolved dependencies.

Output contract

Produce:

  1. A decomposition rationale and explicit non-goals.
  2. A directed acyclic task graph with owner, dependencies, inputs, output contract, write scope, and verification for every task.
  3. A handoff protocol and shared-state policy.
  4. Approval points, timeout and retry limits, escalation routes, and stop conditions.
  5. A synthesis plan that resolves disagreements and verifies the integrated result.
  6. A completion report with evidence, remaining uncertainty, and unused or failed branches.

Workflow

  1. Define one measurable objective and the authority boundary before assigning work.
  2. Decompose by separable outputs, not vague roles. Keep shared mutable state to a minimum and retain tightly coupled steps under one owner.
  3. Draw dependencies and identify the critical path. Parallelize only tasks with independent inputs and non-overlapping side effects. Read orchestration-patterns.md when choosing a topology.
  4. Assign one accountable owner per task. Specify inputs, deliverable format, write scope, validation, deadline or timeout, and what warrants escalation.
  5. Give each agent the minimum context and permissions needed. Include source artifacts, not hidden conclusions, when independent judgment matters.
  6. Require structured handoffs: status, result, evidence, changed state, assumptions, risks, and next dependency. Acknowledge receipt before downstream mutation.
  7. Monitor dependency state and useful progress. Bound retries and debates; do not recursively delegate without a clear capacity and ownership model.
  8. Synthesize centrally or through a named integrator. Resolve conflicting claims from primary evidence, run integration checks, and confirm the original completion criteria.
  9. Close or cancel unused work, record unresolved risks, and return control to the final decision owner.

Use python3 scripts/validate_plan.py plan.json --strict before execution. The command structurally checks dependencies, cycles, ownership, approval references, and numeric execution bounds. Strict mode also fails on warnings, including exact parallel write conflicts, missing timeout/retry bounds, and incomplete retry contracts. It validates declarations only; it cannot verify runtime isolation, authorization, approval authenticity, or actual task behavior.

Safety and permissions

  • Delegation never expands authority. Do not let a child agent perform an action the requester did not authorize.
  • Reserve external messages, purchases, deployments, destructive actions, credential access, and production changes for explicit approval points.
  • Isolate credentials and sensitive context by role; do not broadcast secrets through shared state or handoffs.
  • Use single-writer ownership, branches, transactions, or locks for mutable resources.
  • Preserve user-owned changes and make cancellation recoverable.

Verification

  • Validate the plan is acyclic and every dependency and approval reference resolves. Tag consequential work with consequential, deploy, or external-mutation and require its task-local approval_ref to name an approval point for that task.
  • Confirm concurrently runnable tasks do not write the same file, record, branch, or environment.
  • Check every handoff against its output contract before unblocking dependents.
  • Re-run end-to-end tests or evidence checks after synthesis; individual task success is insufficient.
  • Confirm the final report accounts for all tasks as completed, failed, canceled, or superseded.

Failure handling

  • If an agent stalls, inspect its last evidence, retry once only when the failure is transient, then reassign or collapse the task.
  • If agents disagree, ask each for source-backed claims and let the named integrator adjudicate; do not average incompatible answers.
  • If shared state conflicts, pause writers, preserve both versions, and reconcile through the single owner.
  • If a dependency fails, block or redesign downstream work instead of silently fabricating its input.
  • If coordination overhead exceeds remaining work, stop delegation and complete the critical path under one owner.

Example

For “prepare and implement a cross-platform authentication change,” keep architecture and integration under one owner, delegate independent threat modeling and test-fixture design, assign non-overlapping implementation files only after the interface is frozen, require each handoff to include changed paths and test evidence, gate production configuration behind approval, and have the integrator run the complete suite and reconcile security findings before declaring completion.

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 Multi Agent Orchestration AI skill do?

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

Why use Multi Agent Orchestration on TypingMind?

Because you install it once and use it with any model. Multi Agent Orchestration 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 Multi Agent Orchestration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/seb1n/awesome-ai-agent-skills/tree/main/agent-engineering/multi-agent-orchestration. 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 Multi Agent Orchestration?

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 Multi Agent Orchestration?

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

Is the Multi Agent Orchestration AI skill free?

Yes. It is published on GitHub by seb1n 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.

View all

Set up your own AI workspace now

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