Can I customize the orchestration patterns?

Yes, the orchestrator supports single, sequential, parallel, and hierarchical patterns, which can be configured with automated or manual approval gates.

How does the hierarchical coordination work?

For highly complex tasks, a coordinator agent is assigned to decompose the request, delegate sub-tasks to specialists, and synthesize the final result.

How does this skill help save on token costs?

It analyzes task complexity before execution and only recommends multi-agent workflows when necessary, preventing high-token overhead on simple tasks.

Does it support domain-specific agents?

Absolutely. You can define preferred agents for specific domains like security or performance in your local configuration file to ensure the right specialist handles the job.

Is user approval required for expensive tasks?

Yes, the skill is designed with a 'human-in-the-loop' approach, presenting cost estimates and requiring approval before initiating high-multiplier multi-agent workflows.

Multi-Agent Task Orchestrator

Name: Multi-Agent Task Orchestrator
Author: Cain-Ish

byCain-Ish

Productividad y Flujo de Trabajo

Orchestrates complex development tasks by intelligently routing requests between single or multiple specialist agents based on real-time complexity analysis.

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The Multi-Agent Task Orchestrator serves as an intelligent routing layer for Claude Code, optimizing the balance between execution quality and token efficiency. By analyzing incoming requests, it assigns a complexity score and recommends the most effective workflow pattern—ranging from single-agent processing for simple scripts to hierarchical coordination for massive, multi-domain projects. This skill provides transparent cost-benefit estimates and user approval gates, ensuring developers achieve superior results for complex tasks while avoiding unnecessary token expenditure on routine ones.

Características Principales

Intelligent complexity scoring (0-100) for incoming requests
0 GitHub stars
Transparent token cost-benefit analysis and multipliers
Dynamic pattern routing: Single, Sequential, Parallel, and Hierarchical
Domain-specific specialist agent selection and coordination
Configurable user approval gates for budget management

Casos de Uso

Coordinating multi-specialist reviews for security, performance, and test coverage
Automating complex refactoring tasks that require a chain of dependency steps
Optimizing token budgets by preventing multi-agent overhead on trivial tasks

Acerca de

Características Principales

Intelligent complexity scoring (0-100) for incoming requests
0 GitHub stars
Transparent token cost-benefit analysis and multipliers
Dynamic pattern routing: Single, Sequential, Parallel, and Hierarchical
Domain-specific specialist agent selection and coordination
Configurable user approval gates for budget management

Casos de Uso

Coordinating multi-specialist reviews for security, performance, and test coverage
Automating complex refactoring tasks that require a chain of dependency steps
Optimizing token budgets by preventing multi-agent overhead on trivial tasks