Skill Multiagent Orchestration logo

Skill Multiagent Orchestration

Organization
IAPro-Community
skill-multiagent-orchestration

Use when a task mentions subagents, multiagents, parallel agents, team execution, swarm, delegation, or requires dividing independent engineering work across agents while preserving integration safety and token efficiency.

Overview

PublisherIAPro-Community
RepositoryOrquestrador-Maestro
Skill nameskill-multiagent-orchestration
Stars
63
Forks
11
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by IAPro-Community on GitHub. Read the source before you install it.

Installation

Install the Skill Multiagent 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.
    https://github.com/IAPro-Community/Orquestrador-Maestro/tree/v1/codex/skills/skill-multiagent-orchestration
  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/IAPro-Community/Orquestrador-Maestro.git /tmp/Orquestrador-Maestro
mkdir -p .claude/skills
cp -r /tmp/Orquestrador-Maestro/codex/skills/skill-multiagent-orchestration .claude/skills/skill-multiagent-orchestration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Multiagent 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 Skill Multiagent 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 Skill Multiagent 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.

Multiagent Orchestration

Use this skill to decide whether multiagent execution is worth it, then divide work without creating merge conflicts, duplicated exploration, or excessive token use.

Start Here

Use multiagents only when at least one condition is true:

  • There are 2+ independent workstreams with different files, domains, or verification gates.
  • A broad audit can run in parallel with a local implementation.
  • The user explicitly asks for subagents, multiagents, team, swarm, or parallel execution.
  • A task is large enough that one agent would spend most of its time waiting on search, tests, or independent reviews.

Keep work solo when:

  • The task is a routine Git/VCS operation such as status, diff, add, commit, push, pull, branch inspection, or tagging. The lead agent performs it directly; do not create a Git worker for mechanical commands.
  • The task is formatting, a simple rename, a one-file edit, or running one validation command.
  • The next step is a blocking decision or a single-file change.
  • Agents would edit the same files.
  • The task requires one continuous mental model more than parallel throughput.
  • The overhead of prompts, review, and merge is larger than the work.

Execution Profiles

Fast

  • Agents: 0
  • Use for direct fixes, simple questions, small edits, and one obvious skill.
  • Goal: minimum context and fastest path.

Standard

  • Agents: 0
  • Default profile for normal engineering work.
  • Keep implementation, research, and verification in the lead process unless execution is explicitly promoted to multiagent.

Deep

  • Agents: 0
  • Use for broad or difficult work that needs more reasoning, context, or verification.
  • Deep means greater reasoning depth, not automatic fan-out.

Multiagent

  • Subagents: 1-4
  • This is the only normal execution profile that permits provider fan-out.
  • Use only when there are at least 2 independent, non-overlapping workstreams and parallel execution materially helps.

Team

  • Subagents: 2-4
  • Team is an explicit multiagent workflow, not an automatic consequence of task complexity.
  • Require disjoint ownership and a lead/integration owner.

Delegation Rules

Before spawning agents, write a short split:

  1. What stays with the lead agent.
  2. Which workstreams are independent.
  3. Which files or modules each agent owns.
  4. What each agent must return.
  5. Which validation command proves the integrated result.

Each delegated task must include:

  • Scope: exact files, directories, subsystem, or question.
  • Non-overlap: what the agent must not edit.
  • Output: patch summary, findings, changed paths, tests run, and blockers.
  • Constraint: do not revert or overwrite other agents' changes.
  • Verification: the smallest useful command or evidence for that lane.

Recommended Agent Lanes

Use these lanes when they match the task:

  • explore: map files, symbols, project structure, and likely touchpoints.
  • executor: implement a bounded feature or refactor in owned files.
  • security-reviewer: review auth, secrets, tenancy, RLS, payments, SSRF, injection, and webhook risks.
  • test-engineer: add or fix tests and define validation strategy.
  • code-reviewer: review final diff for regressions and maintainability.
  • designer: UI/UX review or implementation for frontend surfaces.
  • dependency-expert: check external SDK docs, package versions, and integration constraints.
  • verifier: confirm completion claims against commands, diffs, and acceptance criteria.
  • skill-premium-web-experience may use these lanes for genuinely independent research, UX strategy, art direction, motion, implementation, and QA. Keep ownership disjoint and centralize the final integration; do not let multiple agents edit the same component tree simultaneously.

Prefer fewer agents with better scopes over many agents with vague scopes.

Treat reasoning depth and delegation as independent axes. Never select multiagent merely because a task is marked deep, complex, security-sensitive, or long-running. There must be parallelizable work.

SaaS Default Split

For SaaS creation or review, start with:

  • Lead: architecture, integration, final decisions, and conflict resolution.
  • Explorer: project conventions, routes, data model, auth, payment touchpoints.
  • Backend executor: server actions, API routes, billing, webhooks, entitlements.
  • Frontend executor: dashboard/admin UX, states, forms, responsive behavior.
  • Security reviewer: RLS, secrets, payment webhook verification, access control.
  • Test/verifier: lint, typecheck, focused tests, smoke checks.

Only activate payment-specific skills when the project actually uses that provider:

  • AbacatePay -> skill-abacatepay-integration
  • Stripe -> skill-stripe-integration

Only activate security scan skills for owned local repos or explicitly authorized targets:

  • Local repo -> skill-saas-security-scan
  • Hooks/CI gates -> skill-security-hooks
  • Staging/preview URL -> skill-saas-dast-recon

Token Control

  • Load the router and aliases before loading full skill bodies.
  • Load one top-level skill first, then chain only the skills needed by evidence.
  • Do not paste entire catalogs or long file trees into agent prompts.
  • Give agents file paths and exact questions instead of broad context dumps.
  • Ask agents for concise structured results, not full narratives.
  • After agents return, summarize and integrate. Do not replay all agent output.

Conflict Control

  • Assign write ownership before implementation.
  • Keep shared files with the lead agent unless a worker has explicit ownership.
  • If two agents need the same file, sequence the work instead of parallelizing it.
  • Review changed paths before integrating results.
  • Run the highest-signal verification after integration, not only per-agent checks.

Completion Gate

Do not claim multiagent work is complete until:

  • Every agent has returned or been deliberately canceled.
  • Changed paths are reviewed for overlap.
  • The integrated result was validated with project-appropriate commands.
  • Remaining risks are named explicitly.

Related Skills

  • skill-saas-factory
  • skill-ai-orchestration
  • skill-saas-security-scan
  • skill-security-hooks
  • skill-frontend-ux-guardrails
  • skill-unified-analytics

Frequently asked questions

What does the Skill Multiagent Orchestration AI skill do?

Use when a task mentions subagents, multiagents, parallel agents, team execution, swarm, delegation, or requires dividing independent engineering work across agents while preserving integration safety and token efficiency.

Why use Skill Multiagent Orchestration on TypingMind?

Because you install it once and use it with any model. Skill Multiagent 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 Skill Multiagent Orchestration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/IAPro-Community/Orquestrador-Maestro/tree/v1/codex/skills/skill-multiagent-orchestration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Skill Multiagent 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 Skill Multiagent Orchestration?

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

Is the Skill Multiagent Orchestration AI skill free?

Yes. It is published on GitHub by IAPro-Community 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.

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