swarm-advanced

Orchestrate multi-agent workflows with mesh, hierarchical, star, or ring topologies.

Updated Mar 14, 2026
One-click install
npx skills add https://github.com/novatech2210-cmd/ImidusApp --skill swarm-advanced-novatech2210-cmd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/novatech2210-cmd/ImidusApp/tree/main/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/novatech2210-cmd/ImidusApp --skill swarm-advanced-novatech2210-cmd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating complex, multi-agent workflows across research, development, and testing environments is error-prone and time-consuming; this skill provides structured swarm orchestration to streamline cross-functional tasks.

Core Features & Use Cases

  • Patterned swarms (Research, Development, Testing, Analysis) with multiple topology options (mesh, hierarchical, star, ring) for scalable coordination.
  • Role-based agent spawning and management to assemble specialized teams for focused tasks.
  • Phase-driven execution, monitoring, and memory/knowledge management for end-to-end traceability.
  • Rich examples and CLI/code snippets to apply swarm orchestration in real-world projects.

Quick Start

Start an advanced swarm with a mesh topology and up to six agents to begin parallel research orchestration.

Frequently Asked Questions about swarm-advanced

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is swarm orchestration for distributed agent workflows?▼

Swarm orchestration coordinates complex distributed agent workflows by providing topology selection, parallel execution, and fault tolerance across research, development, and testing environments. It uses structured patterns to manage multi-agent coordination end-to-end.

How do I choose the right topology for multi-agent parallel execution?▼

Multi-agent parallel execution supports mesh, hierarchical, star, and ring topologies. Mesh suits peer-to-peer coordination, hierarchical fits role-based spawning, star centralizes control, and ring enables sequential data passing for coordinated agent control.

Can I manage in-memory state and monitor agents during phase-driven execution?▼

Yes, phase-driven orchestration includes in-memory state management and real-time monitoring. It provides end-to-end traceability by tracking agent execution phases and managing shared memory across distributed swarm components.

What's the best way to handle fault tolerance in distributed agent systems?▼

Fault tolerance in distributed agent systems is handled through safe error management and structured swarm patterns. It ensures coordinated agent control continues reliably during phase-driven execution despite individual agent failures.

Does this swarm orchestration approach require specific dependencies or platforms?▼

No external dependencies are required. Swarm orchestration operates through Claude Flow primitives, supporting role-based agent spawning and topology selection directly within research, development, and testing projects without platform restrictions.

When should I not use a mesh topology for parallel agent coordination?▼

Mesh topology may not suit workflows requiring strict centralized control or sequential phase-driven execution. Hierarchical or star topologies better serve role-based spawning when coordinated agent control demands centralized monitoring and fault tolerance.